The Machine Learning and the Physical Sciences workshop aims to provide an informal, inclusive and leading-edge venue for research and discussions at the interface of machine learning (ML) and the physical sciences. This interface spans (1) applications of ML in physical sciences (ML for physics), (2) developments in ML motivated by physical insights (physics for ML), and most recently (3) convergence of ML and physical sciences (physics with ML) which inspires questioning what scientific understanding means in the age of complex-AI powered science, and what roles machine and human scientists will play in developing scientific understanding in the future.

Recent years have seen a tremendous increase in cases where ML models are used for scientific processing and discovery, and similarly, instances where tools and insights from the physical sciences are brought to the study of ML models. The harmonious co-development of the two fields is not a surprise: ML methods have had great success in learning complex representations of data that enable novel modeling and data processing approaches in many scientific disciplines. Indeed, in some sense, ML and physics are concerned with a shared goal of characterizing the true probability distributions of nature. As ML and physical science research becomes more intertwined, questions naturally arise around what scientific understanding is when science is performed with the assistance of complex and highly parameterized models.

The breadth of work at the intersection of ML and physical sciences is answering many important questions for both fields while opening up new ones that can only be addressed by a joint effort of both communities. By bringing together ML researchers and physical scientists who apply and study ML, we expect to strengthen the much needed interdisciplinary dialogue, introduce exciting new open problems to the broader community, and stimulate the production of new approaches to solving challenging open problems in the sciences. Invited talks from leading individuals in both communities will cover the state-of-the-art techniques and set the stage for this workshop, which will also include contributed talks selected from submissions.

A part of the workshop program will be dedicated to a focus area discussing a concrete open question of interest to the community: the role of inductive bias and interpretability in PS with ML, and the role of physically-informed inductive biases in ML models. This critical and ongoing conversation because it gets to the heart of what it means to do science in the era of deep learning. The program will also feature a moderated panel discussion on "Funding and Institutional Support for Machine Learning and Physical Sciences Research" -- a topic of urgent current interest to researchers as the nascent field is rallying for institutional support across university departments, national labs, government-funded AI institutes, and industry. The goal of the panel will be to elucidate for researchers across many career stages the various support mechanisms available for this intersectional field and future prospects.

NeurIPS 2023

The Machine Learning and the Physical Sciences 2023 workshop will be held on December 15, 2023 at the New Orleans Convention Center in New Orleans, USA as a part of the 37th annual conference on Neural Information Processing Systems (NeurIPS). The workshop is planned to take place in a hybrid format inclusive of virtual participation.


All times are local New Orleans time. See also the official NeurIPS workshop schedule.

08:15 - 08:30 Opening remarks

08:30 - 08:55 Invited talk: Benefits of Approximate and Partial Equivariance
Shubhendu Trivedi
08:55 - 09:20 Invited talk: Interpretable deep learning for protein modeling
María Rodríguez Martínez
09:20 - 10:15 Panel: Inductive biases and interpretability in machine learning and the physical sciences
Anuj Karpatne, Joshua Speagle, Shubhendu Trivedi, Savannah Thais (moderator)
10:15 - 10:45 Coffee break ☕️

10:45 - 11:00 Contributed talk: Removing Dust from CMB Observations with Diffusion Models
David Heurtel-Depeiges
11:00 - 12:15 Poster session 1

12:00 - 13:30 Lunch break
See here for a list of food options at the venue
13:30 - 14:00 Invited talk: What's missing? A speculative sketch of the future of machine learning and science
Alexander Alemi
14:00 - 15:15 Poster session 2
Refreshments provided, sponsored by BIDS and IAIFI
15:15 - 15:30 Coffee break ☕️

15:30 - 15:45 Contributed talk: Towards an Astronomical Foundation Model for Stars
Henry Leung
15:45 - 16:00 Contributed talk: KeyCLD: Learning Constrained Lagrangian Dynamics in Keypoint Coordinates from Images
Rembert Daems
16:00 - 16:15 Contributed talk: Ultra Fast Transformers on FPGAs for Particle Physics Experiments
Elham E Khoda
16:15 - 17:15 Panel: Institutional support and funding for machine learning and the physical sciences
Sara Hooker, Jesse Thaler, Max Welling, John Wu (moderator)
17:15 - 17:30 Closing remarks


Plenary speakers and focus area panelists.


Topic: Funding and institutional support for research at the intersection of machine learning and physical sciences.


1 Control-aware echo state networks (Ca-ESN) for the suppression of extreme events [paper][poster]
Racca, Alberto*; Magri, Luca
2 KeyCLD: Learning Constrained Lagrangian Dynamics in Keypoint Coordinates from Images [paper][poster]
Daems, Rembert*; Taets, Jeroen; Wyffels, Francis; Crevecoeur, Guillaume
4 Incorporating Additive Separability into Hamiltonian Neural Networks for Regression and Interpretation [paper][poster]
Zi-Yu, Khoo*; Low, Jonathan Sze Choong; Bressan, Stéphane
6 Extracting an Informative Latent Representation of High-Dimensional Galaxy Spectra [paper][poster][video]
Iwasaki, Daiki*; Cooray, Suchetha; Takeuchi, Tsutomu
8 When Black-box PDE Solvers Meet Deep Learning: End-to-End Mesh Optimization for Efficient Fluid Flow Prediction [paper][poster]
Ma, Shaocong*; Diffenderfer, James; Kailkhura, Bhavya; Zhou, Yi
9 Physics-consistency of infinite neural networks [paper][poster][video]
Ranftl, Sascha*
10 Pay Attention to Mean Fields for Point Cloud Generation [paper][poster]
Käch, Benno*; Melzer-Pellmann, Isabell; Krücker, Dirk
11 Simulation-based Inference for Cardiovascular Models [paper][poster]
Wehenkel, Antoine*; Behrmann, Jens; Miller, Andy; Sapiro, Guillermo; Sener, Ozan; Cuturi, Marco; Jacobsen, Joern-Henrik
13 Fast SoC thermal simulation with physics-aware U-Net [paper][poster]
Lin, Yu-Sheng*; Lin, Li-Song; Chang, Chin-Jui; Lin, Ting-Yu; Pan, Shih-Hong; Yu, Ya-Wen; Yang, Kai-En; Lee, Wei Cheng; Lin, Yi-Chen; Chen, Tai-Yu; Yeh, Jason
14 Unsupervised segmentation of irradiation-induced order–disorder phase transitions in electron microscopy [paper][poster]
Ter-Petrosyan, Arman H*; Bilbrey, Jenna A; Doty, Christina ; Matthews, Bethany; Wang, Le; Du, Yingge; Lang, Eric; Hattar, Khalid ; Spurgeon, Steven
15 Attention-enhanced neural differential equations for physics-informed deep learning of ion transport [paper][poster]
Rehman, Danyal*; Lienhard, John
16 Learning Closure Relations using Differentiable Programming: An Example in Radiation Transport [paper][poster]
Crilly, Aidan*; Duhig, Benjamin; Bouziani, Nacime
18 DFT Hamiltonian Neural Network Training with Semi-supervised Learning [paper][poster]
Cho, Yucheol*; Choi, Guenseok; Ham, Gyeongdo; Shin, Mincheol; Kim, Daeshik
19 CaloLatent: Score-based Generative Modelling in the Latent Space for Calorimeter Shower Generation [paper][poster]
Madula, Thandikire*; Mikuni, Vinicius M
20 Predicting Galaxy Interloper Fraction with Graph Neural Networks [paper][poster]
Massara, Elena *; Villaescusa, Francisco; Percival, Will
24 ML-Enhanced Generalized Langevin Equation for Transient Anomalous Diffusion in Polymer Dynamics [paper][poster]
Cherchi, Gian-Michele*; Dequidt, Alain ; Hauret, Patrice; Guillin, Arnaud; Barra, Vincent; Martzel, Nicolas
26 Ensemble models outperform single model uncertainties and predictions for operator-learning of hypersonic flows [paper][poster]
Leon, Victor; Ford, Noah; Mrema, Honest; Gilbert, Jeffrey; New, Alexander*
28 Discovering Black Hole Mass Scaling Relations with Symbolic Regression [paper][poster]
Jin, Zehao*; Davis, Benjamin
29 Hydrogen Diffusion through Polymer using Deep Reinforcement Learning [paper][poster]
Sang, Tian*; Nomura, Ken-ichi; Nakano, Aiichiro; Kalia, Rajiv; Vashishta, Priya
30 Nonlinear-manifold reduced order models with domain decomposition [paper][poster][video]
Diaz, Alejandro N*; Choi, Youngsoo; Heinkenschloss, Matthias
31 A Multimodal Dataset and Benchmark for Radio Galaxy and Infrared Host Detection [paper][poster][video]
Gupta, Nikhel*
33 PINNs-TF2: Fast and User-Friendly Physics-Informed Neural Networks in TensorFlow V2 [paper][poster]
Akbarian Bafghi, Reza*; Raissi, Maziar
35 Extending Explainable Boosting Machines to Scientific Image Data [paper][poster]
Schug, Daniel; Yerramreddy, Sai; Caruana, Rich; Greenberg, craig; Zwolak, Justyna P*
37 Fast Detection of Phase Transitions with Multi-Task Learning-by-Confusion [paper][poster]
Arnold, Julian; Schäfer, Frank; Loerch, Niels*
38 A Data-Driven, Non-Linear, Parameterized Reduced Order Model of Metal 3D Printing [paper][poster][video]
Brown, Aaron*; Chin, Eric; Choi, Youngsoo; Khairallah, Saad; McKeown, Joseph
39 Evaluating Physically Motivated Loss Functions for Photometric Redshift Estimation [paper][poster]
Engel, Andrew*; Strube, Jan
40 Variational quantum dynamics of two-dimensional rotor models [paper][poster]
Medvidović, Matija*; Sels, Dries
41 Pre-training strategy using real particle collision data for event classification in collider physics [paper][poster]
Kishimoto, Tomoe*; Morinaga, Masahiro; Saito, Masahiko; Tanaka, Junichi
42 Zephyr : Stitching Heterogeneous Training Data with Normalizing Flow for Photometric Redshift Inference [paper][poster]
Sun, Zechang*; Speagle, Joshua S; Huang, Song; Ting, Yuan-Sen; Cai, Zheng
43 Data-Driven Autoencoder Numerical Solver with Uncertainty Quantification for Fast Physical Simulations [paper][poster]
Bonneville, Christophe*; Choi, Youngsoo; Ghosh, Debojyoti; Belof, Jonathan L
45 Uncovering Conformal Towers Using Deep Learning [paper][poster]
Oppenheim, Lior*; Ringel, Zohar; Gazit, Snir; Koch-Janusz, Maciej
47 Incremental learning for physics-informed neural networks [paper][poster]
Dekhovich, Aleksandr; Sluiter, Marcel HF; Tax, David M.J.; Bessa, Miguel A*
49 GAMMA: Galactic Attributes of Mass, Metallicity, and Age Dataset [paper][poster]
Buck, Tobias*; Çakır, Ufuk
50 Differential optimisation for task- and constraint-aware design of particle detectors [paper][poster]
Strong, Giles C*; Lagrange, Maxime; Orio, Aitor; Bordignon, Anna; Bury, Florian; Dorigo, Tommaso; Giammanco, Andrea; Safieldin, Mariam; Kieseler, Jan; Lamparth, Max; Martinez, Pablo; Nardi, Federico; Vischia, Pietro; Zaraket, Haitham
51 Neural ODEs as a discovery tool to characterize the structure of the hot galactic wind of M82 [paper][poster]
Nguyen, Dustin*; Ting, Yuan-Sen; Thompson, Todd; Lopez, Sebastian; Lopez, Laura
52 Speeding up astrochemical reaction networks with autoencoders and neural ODEs [paper][poster]
Buck, Tobias*; Sulzer, Immanuel
53 GalacticFlow: Learning a Generalized Representation of Galaxies with Normalizing Flows [paper][poster]
Buck, Tobias*; Wolf, Luca
54 PACuna: Automated Fine-Tuning of Language Models for Particle Accelerators [paper][poster]
Sulc, Antonin*; Kammering, Raimund; Eichler, Annika; Wilksen, Tim
55 Graph-Theoretical Approaches for AI-Driven Discovery in Quantum Optics [paper][poster]
Gu, Xuemei*; Ruiz-Gonzalez, Carlos; Arlt, Soeren; Jaouni, Tareq; Petermann, Jan ; Sayyad, Sharareh; Karimi, Ebrahim; Tischler, Nora; Krenn, Mario
57 Direct Amortized Likelihood Ratio Estimation [paper][poster]
Cobb, Adam D*; Matejek, Brian; Elenius, Daniel; Roy, Anirban ; Jha, Susmit
59 Physics-informed neural networks with unknown measurement noise [paper][poster]
Pilar, Philipp*; Wahlstroem, Niklas
60 Universal Semantic-less Texture Boundary Detection for Microscopy (and Metallography) [paper][poster][video]
Rusanovsky, Matan; Beeri, Ofer ; Avidan, Shai; Oren, Gal*
61 Information bottleneck learns dominant transfer operator eigenfunctions in dynamical systems [paper][poster]
Schmitt, Matthew S*; Koch-Janusz, Maciej; Fruchart, Michel; Seara, Daniel; Vitelli, Vincenzo
62 Causa prima: cosmology meets causal discovery for the first time [paper][poster][video]
Pasquato, Mario*; Jin, Zehao; Lemos, Pablo; Davis, Benjamin; Macciò, Andrea
64 Unraveling the Mysteries of Galaxy Clusters: Recurrent Inference Deconvolution of X-ray Spectra [paper][poster]
Rhea, Carter*; Hlavacek-Larrondo, Julie; Kraft, Ralph; Bogdan, Akos; Perreault-Levasseur, Laurence; Adam, Alexandre; Zuhone, John
66 Scalable physics-guided data-driven component model reduction for Stokes flow [paper][poster]
Chung, Seung Whan*; Choi, Youngsoo; Roy, Pratanu; Moore, Thomas; Roy, Thomas; Lin, Tiras; Baker, Sarah
67 Improving dispersive readout of a superconducting qubit by machine learning on path signature [paper][poster]
Cao, Shuxiang*; Shao, Zhen; Zheng, Jian-Qing; Bakr, Mustafa; Leek, Peter; Lyons, Terry J
69 Optimizing Likelihood-free Inference using Self-supervised Neural Symmetry Embeddings [paper][poster]
Chatterjee, Deep*; Harris, Philip C; Goel, Maanas; Desai, Malina; Coughlin, Michael; Katsavounidis, Erik
70 Removing Dust from CMB Observations with Diffusion Models [paper][poster]
Heurtel-Depeiges, David; Burkhart, Blakesley; Ohana, Ruben*; Régaldo-Saint Blancard, Bruno
71 Rho-Diffusion: A diffusion-based density estimation framework for computational physics [paper][poster]
Cai, Maxwell X.*; Lee, Kin Long Kelvin
72 Transformers for Scattering Amplitudes [paper][poster][video]
Merz, Garrett W*; Charton, Francois; Cai, Tianji; Cranmer, Kyle; Dixon, Lance; Nolte, Niklas; Wilhelm, Matthias
74 NeuralHMC: Accelerated Hamiltonian Monte Carlo with a Neural Network Surrogate Likelihood [paper][poster][video]
Wolniewicz, Linnea M*; Sadowski, Peter; Corti, Claudio
75 Discovering Galaxy Features via Dataset Distillation [paper][poster]
Guan, Haowen*; Zhao, Xuan; Wang, Zishi; Li, Zhiyang; Kempe, Julia
77 Modeling Coupled 1D PDEs of Cardiovascular Flow with Spatial Neural ODEs [paper][poster][video]
Csala, Hunor*; Mohan, Arvind T; Livescu, Daniel; Arzani, Amirhossein
79 Generating Multiphase Fluid Configurations in Fractures using Diffusion Models [paper][poster]
Chung, Jaehong*; Marcato, Agnese; Guiltinan, Eric; Mukerji, Tapan; Lin, Yen Ting; Santos, Javier E
82 Redefining Super-Resolution: Fine-mesh PDE predictions without classical simulations [paper][poster]
Sarkar, Rajat Kumar*; Majumdar, Ritam; Jadhav, Vishal; Sakhinana, Sagar Srinivas; Runkana, Venkataramana
83 Discovering Quantum Error Correcting Codes with Deep Reinforcement Learning [paper][poster]
Olle, Jan*; Zen, Remmy; Puviani, Matteo; Marquardt, Florian
84 Discovering Quantum Circuits for Logical State Preparation with Deep Reinforcement Learning [paper][poster]
Zen, Remmy*; Olle, Jan; Puviani, Matteo; Marquardt, Florian
85 Differentiable Simulation of a Liquid Argon TPC for High-Dimensional Calibration [paper][poster]
Granger, Pierre*
87 Learning Hard Distributions with Quantum-enhanced Variational Autoencoders [paper][poster][video]
Rao, Anantha S; Madan, Dhiraj*; Ray, Anupama; Vinayagamurthy, Dhinakaran; Santhanam, M S
88 Revealing the Mechanism of Large-scale Gradient Systems Using a Neural Reduced Potential [paper][poster]
Tsuji, Shunya; Murakami, Ryo; Shouno, Hayaru*; Mototake, Yoh-ichi
89 Physical Symbolic Optimization [paper][poster]
Tenachi, Wassim*; Ibata, Rodrigo A; Diakogiannis, Foivos I
90 Score-based Data Assimilation for a Two-Layer Quasi-Geostrophic Model [paper][poster]
Rozet, François*; Louppe, Gilles
91 Physics-Informed Tensor Basis Neural Network for Turbulence Closure Modeling [paper][poster]
Riccius, Leon*; Agrawal, Atul; Koutsourelakis, PS
92 Relating Generalization in Deep Neural Networks to Sensitivity of Discrete Dynamical Systems [paper][poster]
Disselhoff, Jan*; Wand, Michael
94 Orbital-Free Density Functional Theory with Continuous Normalizing Flows [paper][poster]
Vargas Hernández, Rodrigo A.*; Chen, Ricky T Q; de Camargo, Alexandre
95 DeepTreeGANv2: Iterative Pooling of Point Clouds [paper][poster]
Scham, Moritz A.W.*; Krücker, Dirk; Borras, Kerstin
96 Robust Ocean Subgrid-Scale Parameterizations Using Fourier Neural Operators [paper][poster]
Mangeleer, Victor*; Louppe, Gilles
97 3D Localization of Microparticles from Holographic Images using Neural Networks [paper][poster]
Paliwal, Ayush*; Schlenczek, Oliver; Thiede, Birte; Bagheri, Gholamhossein; Ecker, Alexander S
98 Locating Hidden Exoplanets Using Machine Learning [paper][poster]
Terry, Jason P*; Gleyzer, Sergei
99 Learning Optical Maps in Liquid Xenon Detector with Poisson Likelihood Loss [paper][poster]
Liang, Shixiao*; Tunnell, Christopher
102 AstroYOLO: Learning Astronomy Multi-Tasks in a Single Unified Real-Time Framework [paper][poster]
Khujaev, Nodirkhuja; Tsoy, Roman; Baek, Seungryul*
103 Coarse graining systems on inhomogeneous graphs using contrastive learning [paper][poster]
Gökmen, Doruk Efe*; Koch-Janusz, Maciej; Ringel, Zohar; Huber, Sebastian; Flicker, Felix; Biswas, Sounak
104 Understanding Pathologies of Deep Heteroskedastic Regression [paper][poster]
Wong-Toi, Eliot*; Boyd, Alex J; Fortuin, Vincent; Mandt, Stephan
105 Advancing Generative Modelling of Calorimeter Showers on Three Frontiers [paper][poster]
Buhmann, Erik*; Diefenbacher, Sascha; Eren, Engin; Gaede, Frank; Kasieczka, Gregor ; Korcari, William; Korol, Anatolii; Krause, Claudius G; Krueger, Katja; McKeown, Peter; Shekhzadeh, Imahn; Shih, David
106 Multi-fidelity Constrained Optimization for Stochastic Black Box Simulators [paper][poster]
Ravi, Kislaya*; Agrawal, Atul; Koutsourelakis, PS; Bungartz, Hans-Joachim
107 Activation Functions in Non-Negative Neural Networks [paper][poster]
Becker, Marlon; Drees, Dominik; Brückerhoff-Plückelmann, Frank; Schuck, Carsten; Pernice, Wolfram; Risse, Benjamin*
108 Tree-Based Algorithms for Weakly Supervised Anomaly Detection [paper][poster]
Finke, Thorben J; Hein, Marie; Kasieczka, Gregor ; Krämer, Michael; Mück, Alexander; Prangchaikul, Parada; Quadfasel, Tobias*; Shih, David; Sommerhalder, Manuel
109 HIDM: Emulating Large Scale HI Maps using Score-based Diffusion Models [paper][poster]
Hassan, Sultan*; Andrianomena, Sambatra HS
111 Probabilistic Machine Learning based Turbulence Model Learning with a Differentiable Solver [paper][poster]
Agrawal, Atul*; Koutsourelakis, PS
113 AI ensemble for signal detection of higher order gravitational wave modes of quasi-circular, spinning, non-precessing binary black hole mergers [paper][poster]
Tian, Minyang*; Huerta, Eliu A
114 Latent space representations of cosmological fields [paper][poster]
Andrianomena, Sambatra HS*; Hassan, Sultan
115 Enhancing Data-Assimilation in CFD using Graph Neural Networks [paper][poster]
Quattromini, Michele*; Bucci, Michele Alessandro; Cherubini, Stefania; Semeraro, Onofrio
116 Gamma Ray AGNs: Estimating Redshifts and Blazar Classification using Neural Networks with smart initialization and self-supervised learning [paper][poster]
Gharat, Sarvesh*; Bhatta, Gopal; BORTHAKUR, ABHIMANYU
117 Enhancing the local expressivity of geometric graph neural networks [paper][poster]
Norwood, Sam W*; Schaaf, Lars L; Batatia, Ilyes; Bhowmik, Arghya; Csányi, Gábor
121 Domain Adaptation for Measurements of Strong Gravitational Lenses [paper][poster]
Swierc, Paxson*; Zhao, Yifan; Ciprijanovic, Aleksandra; Nord, Brian
123 QDC: Quantum Diffusion Convolution Kernels on Graphs [paper][poster]
Markovich, Thomas*
124 Efficient and Robust Jet Tagging at the LHC with Knowledge Distillation [paper][poster]
Liu, Ryan; Gandrakota, Abhijith*; Ngadiuba, Jennifer; vlimant, jean-roch; Spiropulu , Maria
125 Fast Particle-based Anomaly Detection Algorithm with Variational Autoencoder [paper][poster]
Liu, Ryan*; Gandrakota, Abhijith; Ngadiuba, Jennifer; vlimant, jean-roch; Spiropulu, Maria
129 Preparing Spectral Data for Machine Learning: A Study of Geological Classification from Aerial Surveys [paper][poster]
Chung, Jun Woo*; Sim, Alex; Quiter, Brian; Wu, Yuxin; Zhao, Weijie; Wu, Kesheng
130 Loss-driven sampling within hard-to-learn areas for simulation-based neural network training [paper][poster]
Dymchenko, Sofya*; Raffin, Bruno
131 Long Time Series Data Release from Broadband Axion Dark Matter Experiment [paper][poster]
Fry, Jessica T.*; Li, Aobo; Fu, Xinyi Hope; Winslow, Lindley; Pappas, Kaliroe
132 Physics - Informed Machine Learning for Reduced Space Chemical Kinetics [paper][poster]
Kumar, Anuj*; Echekki, Tarek
133 Smartpixels: Towards on-sensor inference of charged particle track parameters and uncertainties [paper][poster]
Gray, Lindsey A*; Dickinson, Jennet; Kovach-Fuentes, Rachel; Swartz, Morris; Di Guglielmo, Giuseppe; Bean, Alice; Berry, Douglas; Valentin, Manuel Blanco; DiPetrillo, Karri; Fahim, Farah; Hirschauer, Jim; Kulkarni, Shruti; Lipton, Ron; Maksimovic, Petar; Mills, Corrinne; Neubauer , Mark; Parpillon, Benjamin; Pradhan, Gauri; Syal, Chinar; Tran, Nhan; Yoo, Jieun; Young, Aaron
134 On Representations of Mean-Field Variational Inference [paper][poster]
Ghosh, Soumyadip; Lu, Yingdong*; Nowicki, Tomasz; Zhang, Edith J
135 Active learning meets fractal decision boundaries: a cautionary tale from the Sitnikov three body problem [paper][poster]
Payot, Nicolas*; Pasquato, Mario; Trani, Alessandro Alberto; Hezaveh, Yashar; Perreault-Levasseur, Laurence
136 A deep learning framework for jointly extracting spectra and source-count distributions in astronomy [paper][poster]
Wolf, Florian*; List, Florian; Rodd, Nicholas; Hahn, Oliver
137 Machine learning-based compression of quantum many body physics: PCA and autoencoder representation of the vertex function [paper][poster]
Zang, Jiawei*; Medvidović, Matija; Kiese, Dominik; Di Sante, Domenico; Sengupta, Anirvan; Millis, Andy
138 Domain Adaptive Graph Neural Networks for Constraining Cosmological Parameters Across Multiple Data Sets [paper][poster]
Roncoli, Andrea*; Ciprijanovic, Aleksandra; Voetberg, Margaret; Villaescusa, Francisco; Nord, Brian
139 Multibasis Encodings in Recurrent Neural Network Wave Functions for Variational Optimization [paper][poster]
Kokaew, Wirawat*
140 Simulation Based Inference of BNS Kilonova Properties: A Case Study with AT2017gfo [paper][poster]
de matos, Phelipe Antonie Darc*; Bom, Clecio; Fraga, Bernardo M O ; Kilpatrick, Charles D.
141 Physics-aware Modeling of an Accelerated Particle Cloud [paper][poster]
Goutierre, Emmanuel*; Guler, Hayg; Bruni, Christelle; Cohen, Johanne; Sebag, Michele
144 A Physics-Constrained NeuralODE Approach for Robust Learning of Stiff Chemical Kinetics [paper][poster]
Kumar, Tadbhagya*; Kumar, Anuj; Pal, Pinaki
145 Trick or treat? Evaluating stability strategies in graph network-based simulators [paper][poster]
Rochman Sharabi, Omer*; Louppe, Gilles
146 Super-Resolution Emulation of Large Cosmological Fields with a 3D Conditional Diffusion Model [paper][poster]
Rouhiainen, Adam*; Gira, Michael; Shiu, Gary; Lee, Kangwook; Münchmeyer, Moritz
147 E(2) Equivariant Neural Networks for Robust Galaxy Morphology Classification [paper][poster]
Pandya, Sneh J*; Patel, Purvik; O, Franc; Blazek, Jonathan
148 Reduced-order modeling for parameterized PDEs via implicit neural representations [paper][poster][video]
Wen, Tianshu*; Lee, Kookjin; Choi, Youngsoo
149 Simulation-Based Inference for Detecting Blending in Spectra [paper][poster]
McNamara, Declan*; Regier, Jeffrey
150 JetLOV: Enhancing Jet Tree Tagging through Neural Network Learning of Optimal LundNet Variables [paper][poster]
Cerro, Giorgio*
151 Hierarchical Cross-entropy Loss for Classification of Astrophysical Transients [paper][poster]
Villar, Victoria A*
152 Surrogate Model Training Data for FIDVR-related Voltage Control in Large-scale Power Grids [paper][poster]
Yin, Tianzhixi*; Huang, Renke; Hossain, Ramij-Raja; Huang, Qiuhua; Tan, Jie; Yu, Wenhao
154 Differentiable, End-to-End Forward Modeling for 21 cm Cosmology: Robust Systematics Error Budgeting and More [paper][poster]
Kern, Nicholas*
155 Investigating the Ability of PINNs To Solve Burgers’ PDE Near Finite-Time BlowUp [paper][poster][video]
Kumar, Dibyakanti*; Mukherjee, Anirbit
156 Detection and Segmentation of Ice Blocks in Europa's Chaos Terrain Using Mask R-CNN [paper][poster]
Dunn, Marina M*; Nixon, Conor A; Mills, Alyssa C; Awadallah, Ahmed; Duncan, Ethan J; Santerre, John W; Trent, Douglas; Larsen, Andrew
157 Neural Networks vs. Whittaker Smoothing: Advanced Techniques for Scattering Signal Removal in 3D Fluorescence spectra [paper][poster]
Zakuskin, Aleksandr; Krylov, Ivan N.; Labutin, Timur A.*
159 Benchmarking of Fast and Interpretable UF Machine Learning Potentials [paper][poster]
Prakash, Pawan*
160 A Physics-Informed Variational Autoencoder for Rapid Galaxy Inference and Anomaly Detection [paper][poster]
Gagliano, Alexander T*; Villar, Ashley
162 Pythia: A prototype artificial agent for designing optimal gravitational-wave follow-up campaigns [paper][poster]
Sravan, Niharika*; Graham, Matthew; Coughlin, Michael; Anand, Shreya; Ahumada, Tomas
163 Probabilistic Reconstruction of Dark Matter fields from galaxies using diffusion models [paper][poster]
Cuesta, Carolina; Ni, Yueying; Park, Core Francisco; Mudur, Nayantara; Ono, Victoria*
164 Predicting the Age of Astronomical Transients from Real-Time Multivariate Time Series [paper][poster]
Muthukrishna, Daniel*
165 Multiscale Feature Attribution for Outliers [paper][poster]
Shen, Jeff*; Melchior, Peter M
167 Learning Reionization History from Quasars with Simulation-Based Inference [paper][poster]
Chen, Huanqing*; Speagle, Joshua S; Rogers, Keir
168 Interpretable Joint Event-Particle Reconstruction at NOvA with Sparse CNNs and Transformers [paper][poster]
Shmakov, Alexander*; Yankelevich, Alejandro J; Bian, Jianming; Baldi, Pierre
170 SimSIMS: Simulation-based Supernova Ia Model Selection with thousands of latent variables [paper][poster]
Karchev, Kosio*; Trotta, Roberto ; Weniger, Christoph
171 Accelerating Kinetic Simulations of Electrostatic Plasmas with Reduced-Order Modeling [paper][poster][video]
Tsai, Ping-Hsuan*; Chung, Seung Whan; Ghosh, Debojyoti; Loffeld, John; Choi, Youngsoo; Belof, Jonathan L
172 Sequential Monte Carlo for Detecting and Deblending Objects in Astronomical Images [paper][poster]
White, Tim*; Regier, Jeffrey
173 DeepSurveySim: Simulation Software and Benchmark Challenges for Astronomical Observation Scheduling [paper][poster]
Voetberg, Margaret*; Nord, Brian
174 LoDIP: Low-dose phase retrieval with deep image prior [paper][poster]
Manekar, Raunak*; Negrini, Elisa; Pham, Minh; Jacobs, Daniel; Srivastava, Jaideep; Osher, Stanley; Miao, Jianwei
175 Bayesian multi-band fitting of alerts for kilonovae detection [paper][poster]
Biswas, Biswajit*
176 Forward Gradients for Data-Driven CFD Wall Models [paper][poster]
Hueckelheim, Jan C*; Kumar, Tadbhagya; Raghavan, Krishnan; Pal, Pinaki
177 Learning an Effective Evolution Equation for Particle-Mesh Simulations Across Cosmologies [paper][poster]
Payot, Nicolas*; Lemos, Pablo; Perreault-Levasseur, Laurence; Cuesta, Carolina; Modi, Chirag; Hezaveh, Yashar
178 Active Learning for Discovering Complex Phase Diagrams with Gaussian Processes [paper][poster]
Zhu, Max Y*; Yao, Jian; Mynatt, Marcus; Pugzlys, Hubert; Li, Shuyi; Zhao, Qingyuan; Jia, Chunjing
179 RACER: Rational Artificial Intelligence Car-following-model Enhanced by Reality [paper][poster]
Li, Tianyi*; Stern, Raphael
180 Learned integration contour deformation for signal-to-noise improvement in Monte Carlo calculations [paper][poster]
Detmold, William; Kanwar, Gurtej; Lin, Yin*; Shanahan, Phiala; Wagman, Michael
181 The search for the lost attractor [paper][poster][video]
Pasquato, Mario*; Haddad, Syphax; Di Cintio, Pierfrancesco; Adam, Alexandre; Dia, Noé; Petrache, Mircea; Di Carlo, Ugo Niccolò; Trani, Alessandro Alberto; Perreault-Levasseur, Laurence; Hezaveh, Yashar; Lemos, Pablo
182 Cosmological Field Emulation and Parameter Inference with Diffusion Models [paper][poster]
Mudur, Nayantara*; Cuesta, Carolina; Finkbeiner, Douglas
183 Symbolic Machine Learning for High Energy Physics Calculations [paper][poster]
Alnuqaydan, Abdulhakim*; Gleyzer, Sergei; Prosper, Harrison; Reinhardt, Eric; Charton, Francois; Anand, Neeraj
184 Autoencoding Labeled Interpolator, Inferring Parameters From Image And Image From Parameters [paper][poster]
SaraerToosi, Ali*; Broderick, Avery
185 Leveraging Deep Learning for Physical Model Bias of Global Air Quality Estimates [paper][poster]
Doerksen, Kelsey*; Gal, Yarin; Kalaitzis, Freddie; Marchetti, Yuliya; Lu, You; Montgomery, James; Miyazaki, Kazuyuki; Bowman, Kevin
186 Towards data-driven models of hadronization [paper][poster][video]
Bierlich, Christian; Ilten, Phil; Menzo, Tony; Mrenna, Stephen; Szewc, Manuel; Wilkinson, Michael K. ; Youssef, Ahmed*; Zupan, Jure
187 From Plateaus to Progress: Unveiling Training Dynamics of PINNs [paper][poster]
Lengyel, Daniel*; Parpas, Panos; Pandya, Rahil R
188 Equivariant Neural Networks for Signatures of Dark Matter Morphology in Strong Lensing Data [paper][poster]
Cheeramvelil, Geo Jolly*; Toomey, Michael W; Gleyzer, Sergei
189 Echoes in the Noise: Posterior Samples of Faint Galaxy Surface Brightness Profiles with Score-Based Likelihoods and Priors [paper][poster]
Adam, Alexandre*; Stone, Connor J; Bottrell, Connor; Legin, Ronan; Perreault-Levasseur, Laurence; Hezaveh, Yashar
190 Deep Learning Segmentation of Spiral Arms and Bars [paper][poster]
Walmsley, Mike*; Spindler, Ashley
191 Accelerating Flow Simulations using Online Dynamic Mode Decomposition [paper][poster][video]
Suh, Seung Won*; Chung, Seung Whan; Bremer, Peer-Timo; Choi, Youngsoo
192 Sparse 3D Images: Point Cloud or Image methods? [paper][poster]
Torales Acosta, Fernando*; Mikuni, Vinicius M; Nachman, Benjamin; Arratia, Miguel; Karki, Bishnu; Milton, Ryan; Karande, Piyush; Angerami, Aaron
194 Classification under Prior Probability Shift in Simulator-Based Inference: Application to Atmospheric Cosmic-Ray Showers [paper][poster]
Shen, Alexander*; Lee, Ann; Masserano, Luca; Dorigo, Tommaso; Doro, Michele; Izbicki, Rafael
195 Rare Galaxy Classes Identified In Foundation Model Representations [paper][poster]
Walmsley, Mike*; Scaife, Anna
196 Understanding and Visualizing Droplet Distributions in Simulations of Shallow Clouds [paper][poster]
Will, Justus C*; Jenney, Andrea; Lamb, Kara D.; Pritchard, Michael; Kaul, Colleen; Ma, Po-Lun; Shpund, Jacob; Pressel, Kyle; van Lier-Walqui, Marcus; Mandt, Stephan
197 Beyond PID Controllers: PPO with Neuralized PID Policy for Proton Beam Intensity Control in Mu2e [paper][poster]
Hu, Jerry Yao-Chieh; Xu, Chenwei*; Narayanan, Aakaash; Thieme, Mattson; Nagaslaev, Vladimir; Austin, Mark; Arnold, Jeremy; Berlioz, Jose; Hanlet, Pierrick; Ibrahim, Aisha; Nicklaus, Dennis; Mitrevski, Jovan; Pradhan, Gauri; Saewert, Andrea; Seiya, Kiyomi; Schupbach, Brian; Thurman-Keup, Randy; Tran, Nhan; Shi, Rui; Ogrenci, Seda; Shuping, Alexis Maya-Isabelle ; Hazelwood, Kyle; Liu, Han
200 Loss Functionals for Learning Likelihood Ratios [paper][poster]
Rizvi, Shahzar*; Pettee, Mariel; Nachman, Benjamin
201 19 Parameters Is All You Need: Tiny Neural Networks for Particle Physics [paper][poster]
Bogatskiy, Alexander*; Hoffman, Timothy; Offermann, Jan
202 CP-PINNs: Changepoints Detection in PDEs using Physics Informed Neural Networks with Total-Variation Penalty [paper][poster]
Dong, Zhikang*; Polak, Pawel
203 Self-Driving Telescopes: Autonomous Scheduling of Astronomical Observation Campaigns with Offline Reinforcement Learning [paper][poster]
Terranova, Franco*; Voetberg, Margaret; Nord, Brian; Pagul, Amanda
204 High-dimensional and Permutation Invariant Anomaly Detection with Diffusion Generative Models [paper][poster]
Mikuni, Vinicius M*; Nachman, Benjamin
205 Generative Diffusion Models for Lattice Field Theory [paper][poster]
Wang, Lingxiao*; Aarts, Gert; Zhou, Kai
206 Reconstruction of Fields from Sparse Sensing: Differentiable Sensor Placement Enhances Generalization [paper][poster]
Marcato, Agnese*; O'Malley, Daniel; Viswanathan, Hari S; Guiltinan, Eric; Santos, Javier E
207 Learning Dark Matter Representation from Strong Lensing Images through Self-Supervision [paper][poster]
Deshmukh, Yashwardhan A.*; Sachdev, Kartik; Toomey, Michael W; Gleyzer, Sergei
209 Graph Neural Networks for Identifying Protein Reactive Compounds [paper][poster]
Cano Gil, Victor Hugo*; Rowley, Christopher
210 Towards out-of-distribution generalization in large-scale astronomical surveys: robust networks learn similar representations [paper][poster]
Gondhalekar, Yash*; Hassan, Sultan; Saphra, Naomi P; Andrianomena, Sambatra HS
211 Towards an Astronomical Foundation Model for Stars [paper][poster]
Leung, Henry*
213 Induced Generative Adversarial Particle Transformers [paper][poster]
Li, Anni; Krishnamohan, Venkat; Kansal, Raghav*; Duarte, Javier; Sen, Rounak; Tsan, Steven; Zhang, Zhaoyu
214 Lensformer: A Physics-Informed Vision Transformer for Gravitational Lensing [paper][poster]
Velôso de Souza, Lucas José*; Toomey, Michael W; Gleyzer, Sergei
215 Self-supervised learning for searching jellyfish galaxies in the ocean of data from upcoming surveys [paper][poster]
Gondhalekar, Yash*; de Souza, Rafael; Chies Santos, Ana; Queiroz de Abreu Silva, Carolina
216 deep-REMAP: Parameterization of Stellar Spectra Using Regularized Multi-Task Learning [paper][poster]
Gilda, Sankalp*
218 Bayesian Simulation-based Inference for Cosmological Initial Conditions [paper][poster]
Anau Montel, Noemi*; List, Florian; Weniger, Christoph
220 Autoregressive Transformers for Disruption Prediction in Nuclear Fusion Plasmas [paper][poster]
Spangher, Lucas*; Arnold, William F; Spangher, Alexander; Maris, Andrew; Rea, Cristina
221 CaloFFJORD: High Fidelity Calorimeter Simulation Using Continuous Normalizing Flows [paper][poster]
Furia, Chirag*; Mikuni, Vinicius M
222 Machine learning-assisted nanoscale photoelectrical sensing [paper][poster]
Zhu , Ziyan*; Ji, Zhurun; Yassin, Houssam; Shen, Zhi-Xun; Devereaux, Thomas
223 Emulating deviations from Einstein's General Relativity using conditional GANs [paper][poster]
Gondhalekar, Yash*; Bose, Sownak
225 Operator SVD with Neural Networks via Nested Low-Rank Approximation [paper][poster]
Ryu, Jongha J; Xu, Xiangxiang; Erol, Hasan Sabri Melihcan; Bu, Yuheng; Zheng, Lizhong; Wornell, Gregory W*
227 Gradient weighted physics-informed neural networks for capturing shocks in porous media flows [paper][poster]
Kapoor, Somiya; Chandra, Abhishek; Kapoor, Taniya*; Curti, Mitrofan
229 Physics-Informed Calibration of Aeromagnetic Compensation in Magnetic Navigation Systems using Liquid Time-Constant Networks
Nerrise, Favour*; Sosanya, Sosa; Neary, Patrick
230 The DL Advocate: Playing the devil’s advocate with hidden systematic uncertainties [paper][poster]
Ustyuzhanin, Andrey*; Golutvin, Andrey; Iniukhin, Alexander; Owen, Patrick; Mauri, Andrea; Serra, Nicola
231 MCMC to address model misspecification in Deep Learning classification of Radio Galaxies [paper][poster]
Mohan, Devina*; Scaife, Anna
232 Application of Zone Method based Physics-Informed Neural Networks in Reheating Furnaces [paper][poster]
Dutta, Ujjal Kr*; Lipani, Aldo; Wang, Chuan; Hu, Yukun
233 LEO Satellite Orbit Prediction with Physics Informed Machine Learning [paper][poster]
Alesiani, Francesco*; Takamoto, Makoto; Kamiya, Toshio; Etou, Daisuke
234 Physically Accurate Fast Nanophotonic Simulations with Physics Informed Model and Training [paper][poster]
Dasdemir, Ahmet Onur; Dimici, Can; Erdem, Aykut; Magden, Emir Salih*
235 Bayesian Imaging for Radio Interferometry with Score-Based Priors [paper][poster]
Dia, Noé*; Yantovski-Barth, M. J. ; Adam, Alexandre; Bowles, Micah R; Lemos, Pablo; Perreault-Levasseur, Laurence; Hezaveh, Yashar; Scaife, Anna
236 Virtual EVE: a Deep Learning Model for Solar Irradiance Prediction [paper][poster]
Indaco, Manuel*; Gass , Daniel ; Fawcett, William; Galvez, Richard; Wright, Paul J; Muñoz-Jaramillo, Andrés
237 High-Cadence Thermospheric Density Estimation enabled by Machine Learning on Solar Imagery [paper][poster]
Malik, Shreshth A*; Walsh, James EJ; Acciarini, Giacomo; Berger, Thomas; Baydin, Atilim Gunes
239 Combining astrophysical datasets with CRUMB [paper][poster]
Porter, Fiona M*
241 Ultra Fast Transformers on FPGAs for Particle Physics Experiments [paper][poster]
Jiang, Zhixing; Yin, Ziang; Khoda, Elham E*; Loncar, Vladimir; Govorkova, Ekaterina; Moreno, Eric A; Harris, Philip C; Hauck, Scott; Hsu, Shih-chieh
242 Unleashing the Potential of Fractional Calculus in Graph Neural Networks [paper][poster]
Kang, Qiyu*; ZHAO, KAI; Ding, Qinxu; Ji, Feng; Li, Xuhao; LIANG, WENFEI; Song, Yang; Tay, Wee Peng
245 Approximately-invariant neural networks for quantum many-body physics [paper][poster]
Kufel, Dominik S*; Kemp, Jack; Yao, Norman
246 Pseudotime Diffusion [paper][poster]
Moss, Jacob*; England, Jeremy; Lió, Pietro
248 Reinforcement Learning for Ising Model [paper][poster]
Lu, Yichen; Liu, Xiao-Yang*
249 Computing Partition Functions in Unnormalized Density Models Using Bayesian Thermodynamic Integration [paper][poster]
Lobashev, Alexander*; Tamm, Mikhail
250 ELUQuant: Event-Level Uncertainty Quantification using Physics-Informed Bayesian Neural Networks with Flow approximated Posteriors - A DIS Study [paper][poster]
Fanelli, Cristiano *; Giroux, James

APL Poster Awards

Sponsored by APL Machine Learning.
  • Classification under Prior Probability Shift in Simulator-Based Inference: Application to Atmospheric Cosmic-Ray Showers [poster] by Alexander Shen; Ann Lee; Luca Masserano; Tommaso Dorigo; Michele Doro; Rafael Izbicki.
  • Graph-Theoretical Approaches for AI-Driven Discovery in Quantum Optics [poster] by Xuemei Gu; Carlos Ruiz-Gonzalez; Soeren Arlt; Tareq Jaouni; Jan Petermann; Sharareh Sayyad; Ebrahim Karimi; Nora Tischler; Mario Krenn.

Program Committee (Reviewers)

We acknowledge the 295 members of the program committee for providing reviews on a very tight schedule and making this workshop possible. They are listed in alphabetical order below.

Aashwin Mishra (Stanford University), Abhijith Gandrakota (Fermilab), Abhinanda Ranjit Punnakkal (UiT The Arctic University of Norway), Abhishek Abhishek (UBC), Abhishek Chandra (Eindhoven University of Technology), Adrian Bayer (UC Berkeley), Adrian Perez-Suay (IPL - Universitat de València), Agnimitra Dasgupta (University of Southern California), Aidan Chambers (IAIFI), Akshay Subramaniam (NVIDIA), Aleksandra Ciprijanovic (Fermilab), Alexander Gagliano (The NSF Institute for Artificial intelligence and Fundamental Interactions), Alexander Sun (The University of Texas at Austin), Alexandre Adam (Université de Montréal), Alexandre Szenicer (X), Alison Appling (U.S. geological survey), Ameya Daigavane (Massachusetts Institute of Technology), Amin Tavakoli (UC Irvine), Andreas Mardt (Redesign Science ), Andreas Schachner (University of Cambridge), Andres Vicente Arevalo (Instituto de Astrofísica de Canarias (IAC)), Andrey Popov (University of Texas at Austin), Andrey Ustyuzhanin (Constructor University, Bremen), Anindita Maiti (Northeastern University), Ankita Shukla (Arizona State University), Anna Jungbluth (European Space Agency), Antoine Wehenkel (Apple), Anurag Saha Roy (Saarland University), Arka Daw (Virginia Tech), Armi Tiihonen (Aalto University), Arnaud Delaunoy (University of Liege), Arnaud Vadeboncoeur (University of Cambridge ), Arun Ross (Michigan State University), Arvind Renganathan (University of Minnesota), Aryya Gangopadhyay (UMBC), Asif Khan (University of Edinburgh), Atul Agrawal (Technical University of Munich), Auralee Edelen (SLAC National Accelerator Laboratory), Austen Lamacraft (University of Cambridge), Babak Rahmani (EPFL), Batuhan Koyuncu (Saarland University), Benjamin Cash (George Mason University), Bilal Thonnam Thodi (New York University Abu Dhabi), Biprateep Dey (University of Pittsburgh), Biwei Dai (University of California, Berkeley), Boyu Zhang (University of Idaho), Bruno Raffin (University of Grenoble), Carolina Cuesta (MIT), Carter Rhea (University of Montreal), Cenk Tüysüz (DESY), Cesar Quilodran-Casas (Imperial College London), Chenyang Li (Argonne National Laboratory), Chirag Modi (Flatiron Institute), Christian Glaser (Uppsala University), Christoph Weniger (University of Amsterdam), Christophe Bonneville (Cornell University), Christopher Hall (RadiaSoft LLC), Conrad Albrecht (German Aerospace Center), Constantin Weisser (QuantumBlack), Cristina Garcia-Cardona (Los Alamos National Laboratory), Daniel Murnane (Lawrence Berkeley National Laboratory), Daniel Serino (LANL), Danielle Maddix (Amazon Research ), Danyal Rehman (Massachusetts Institute of Technology (MIT)), Deep Chatterjee (Massachusetts Institute of Technology), Digbalay Bose (University of Southern California), Dion Häfner (Pasteur Labs & ISI), Elham E Khoda (University of Washington), Eliane Maalouf (University of Neuchâtel), Elyssa Hofgard (Massachusetts Institute of Technology), Emanuele Usai (University of Alabama), Emma Benjaminson (Carnegie Mellon University), Engin Eren (DESY), Fabian Ruehle (Northeastern University), Fatih Dinc (Stanford University), Felix Wagner (HEPHY Vienna), Fernando Romero-Lopez (MIT), Francisco Förster (Millennium Institute of Astrophysics), Franco Pellegrini (École normale supérieure, Paris), Francois Lanusse (CEA Saclay), François Rozet (University of Liège), Gaia Grosso (IAIFI), Gal Oren (Technion), Garrett Merz (University of Wisconsin-Madison), George Stein (UC Berkeley), Gert-Jan Both (CRI), Guillermo Cabrera-Vives (University of Concepción), Hannes Stärk (Massachusetts Institute of Technology), Hao Wu (Shanghai Jiaotong University), Harold Erbin (MIT, IAIFI, CEA-LIST), Hector Corzo ( Center for Chemical Computation and Theory at UC Merced), Henning Kirschenmann (University of Helsinki), Hugo Frezat (Univ. Grenoble Alpes), Hunor Csala (University of Utah), Huziel Sauceda (Technische Universität Berlin), Hyungjin Chung (KAIST), Ieva Kazlauskaite (University of Cambridge), Ion Matei (PARC), Irina Espejo Morales (New York University), Jack Collins (SLAC National Lab), Jaegul Choo (Korea Advanced Institute of Science and Technology), JAÏ Otman (Sidi Mohamed Ben Abdellah University ), Jared Willard (University of Minnesota), jean-roch vlimant (California Institute of Technology), Jianan Zhou (Nanyang Technological University), Jie Bu (Virginia Tech), Jiequn Han (Flatiron Institute), Jingyi Tang (Stanford University), Jochen Garcke (University Bonn), Joel Dabrowski (CSIRO), John Martyn (Massachusetts Institute of Technology), John Wu (Space Telescope Science Institute), Jonas Köhler (Free University of Berlin), Jonathan Edelen (RadiaSoft LLC), Jonghyun Lee (University of Hawaii at Manoa), Jordi Cortés-Andrés (ISP-IPL), Jordi Tura (Leiden University), Jose Napoles-Duarte (Universidad Autonoma de Chihuahua), Jose Ruiz-Munoz (Universidad Nacional de Colombia), Joshua Bloom (UC Berkeley), Joshua Yao-Yu Lin (Prescient Design/Genentech), Julian Suk (University of Twente), Junichi Tanaka (ICEPP, The University of TOkyo), Junze Liu (University of California, Irvine), Justine Zeghal (APC CNRS), Kai Fukami (University of California, Los Angeles), Kai Zhou (Frankfurt Institute for Advanced Studies), Karan Shah (Center for Advanced Systems Understanding (CASUS)), Karolos Potamianos (University of Oxford), Katherine Fraser (Harvard University), Kathleen Champion (University of Washington), Katrin Heitmann (Argonne National Laboratory), Keith Brown (Boston University), Keming Zhang (UC Berkeley), Ken-ichi Nomura (University of Southern California), Khoo Zi-Yu (National University of Singapore), Kim Nicoli (TU Berlin), Kunal Ghosh (Aalto University), Kushal Tirumala (FAIR), Lalit Ghule (Ansys Inc.), Lars Doorenbos (University of Bern), Leander Thiele (Princeton University), Lei Wang (IOP, CAS), Li Yang (Google Research), Lijing Wang (New Jersey Institute of Technology), Lin Li (MIT Lincoln Laboratory), Lingxiao Wang (Frankfurt Institute for Advanced Studies), Luc Le Pottier (University of California, Berkeley), Luca Biggio (ETH Zürich), Lucas Meyer (INRIA), Ludger Paehler (Technical University of Munich), M. Maruf (Virginia Tech), Madhurima Nath (Slalom Consulting, LLC), Mai Nguyen (University of California San Diego), Maksim Zhdanov (Helmholtz-Zentrum Dresden-Rossendorf), Manuel Sommerhalder (Universität Hamburg), Marcin Pietroń (AGH UST), Mariano Dominguez (IATE), Mariel Pettee (Lawrence Berkeley National Lab), Mario Krenn (Max Planck Institute for the Science of Light), Marios Mattheakis (E Ink), Masaki Adachi (University of Oxford), Matija Medvidović (Columbia University), Matt Sampson (Princeton University), Matteo Manica (IBM Research), Matthew Spellings (Vector Institute), Max Zhu (University of Cambridge), Maximilian Dax (MPI for Intelligent Systems, Tübingen), Maxwell Cai (Intel Corporation), Maziar Raissi (University of Colorado Boulder), Mehmet Noyan (Ipsumio B.V.), Menachem Stern (University of Pennsylvania), Micah Bowles (The University of Manchester), Michael Douglas (Harvard CMSA), Michelle Kuchera (Davidson College), Mike Williams (Massachusetts Institute of Technology), Mikel Landajuela (Lawrence Livermore National Laboroatory), Milind Malshe (Georgia Institute of Technology), Mit Kotak (Massachusetts Institute of Technology), Mohammad Sultan (Insitro), Mohannad Elhamod (Virginia Tech), Mridul Khurana (Virginia Tech), Muhammad Kasim (Machine Discovery), Namid Stillman (Simudyne), Natalie Klein (Los Alamos National Laboratory), Nayantara Mudur (Harvard University), Neerav Kaushal (Michigan Technological University), Negin Forouzesh (California State University, Los Angeles), Nesar Ramachandra (Argonne National Laboratory), Nick McGreivy (Princeton University), Nils Thuerey (Technical University of Munich), Nishan Srishankar (WPI), Noemi Anau Montel (GRAPPA Institute (University of Amsterdam)), Olivier Saut (CNRS), Omar Alterkait (Tufts University/ IAIFI), Ondrej Hovorka (University of Southampton), Othmane Rifki (Spectrum Labs), Ouail Kitouni (Massachusetts Institute of Technology), Pablo Martin (), Pao-Hsiung Chiu (Institute of High Performance Computing), Paul Atzberger (University of California Santa Barbara), Paula Harder (Fraunhofer ITWM), Pedro L. C. Rodrigues (Inria), Peer-Timo Bremer (LLNL), Peter Harrington (Lawrence Berkeley National Laboratory (Berkeley Lab)), Peter Melchior (Princeton University), Peter Sadowski (University of Hawaii Manoa), Peter Steinbach (HZDR), Pietro Vischia (Université catholique de Louvain), PS Koutsourelakis (TUM), Pulkit Khandelwal (University of Pennsylvania), Qi Tang (Los Alamos National Laboratory), Raffaele Santagati (Boehringer-Ingelheim), Rajat Arora (Advanced Micro Devices (AMD)), Raunak Borker (Ansys), Redouane Lguensat (IPSL), Reza Akbarian Bafghi (University of Colorado Boulder), Rhys Goodall (Chemix.ai), Riccardo Alessandri (University of Chicago), Rikab Gambhir (MIT), Rodrigo A. Vargas Hernández (McMaster University), Sam Foreman (Argonne National Laboratory), Sandeep Madireddy (Argonne National Laboratory), Sankalp Gilda (ML Collective), Sanmay Ganguly (University of Tokyo), Sarvesh Gharat (IIT Bombay), Sascha Caron (Radboud University Nijmegen), Sebastian Dorn (Max-Planck Institute), Sébastien Fabbro (NRC Herzberg Astronomy and Astrophysics), Sergey Shirobokov (Twitter), Shah Nawaz (German Electron Synchrotron), Shanwu Li (Michigan Technological University), Shenao Yan (University of Connecticut), Shinjae Yoo (Brookhaven National Laboratory), Shirley Ho (Flatiron Institute), Shiyu Wang (Emory University), Shriram Chennakesavalu (Stanford University), Shuai Liu (Meta Platform), Siddharth Mishra-Sharma (MIT), Sifan Wang (University of Pennsylvania), Simon Schnake (Deutsches Elektronen-Synchrotron DESY), Sokratis Trifinopoulos (MIT), Somya Sharma (U. Minnesota), Soronzonbold Otgonbaatar (German Aerospace Center, Oberpfaffenhofen and LMU Munich), Srinandan Dasmahapatra (University of Southampton), Stephan Günnemann (Technical University of Munich), Stephen Webb (RadiaSoft LLC), Sui Tang (UCSB), Suryanarayana Maddu (Flatiron Institute/Simons Foundation), Takashi Matsubara (Osaka University), Tatiana Likhomanenko (Apple), Thomas Beckers (University of Pennsylvania), Tobias Buck (IWR), Tobias Liaudat (Commisariat à l'Energie Atomique (CEA)), Tomás Müller (University of Southampton), Tomasz Szumlak (AGH University of Science and Technology), Tomo Lazovich (Lightmatter), Tri Nguyen (MIT), Tristan Bereau (Heidelberg University), Vahe Gharakhanyan (Columbia University), Valentina Salvatelli (IQVIA), Varun Kelkar (University of Illinois at Urbana-Champaign), Victoria Villar (Columbia University), Viktor Podolskiy (Department of Physics and Applied Physics, University of Massachusetts Lowell), Vinayak Bhat (University of Kentucky), Vinicius Mikuni (NERSC), VISHAL DEY (The Ohio State University), Vitus Benson (Max-Planck-Institute for Biogeochemistry), Wai Tong Chung (Stanford University), Wonmin Byeon (NVIDIA Research), Wujie Wang (Massachusetts Institute of Technology), Xian Yeow Lee (Iowa State University), Xiangyang Ju (LBNL), Xiaolong Li (University of Delaware), Xiaowei Jia (University of Pittsburgh), Xiaoyong Jin (Argonne National Laboratory), Xinyan Li (IQVIA), Yao Fehlis (AMD), Yilin Chen (Stanford University), Yin Li (Flatiron Institute), Yingdong Lu (IBM Research AI), Yingtao Luo (Carnegie Mellon University), Yitian Sun (MIT), Youngwoo Cho (Korea Advanced Institute of Science and Technology), Yu Wang (University of Michigan), Yuan Yin (Sorbonne Université, CNRS, ISIR, F-75005 Paris, France), Yuanqi Du (Cornell University), Yunxuan Li (Google LLC), Yuqi Nie (Princeton University), Zeviel Imani (Tuft / IAIFI), Zhe Jiang (University of Florida), zhibo zhang (KLA), Zhikang Dong (Stony Brook University), Ziming Liu (MIT), Zixing Song (The Chinese University of Hong Kong)

Call for papers

In this workshop, we aim to bring together physical scientists and machine learning researchers who work at the intersection of these fields by

  • Applying machine learning to problems in the physical sciences – physics, chemistry, astronomy, materials science, biophysics, and related sciences; and
  • Using physical insights to understand and/or improve machine learning techniques.
In this year's workshop, there are two (2) tracks:

Research Track

We invite researchers to submit original work in the following areas or areas related to them.
  • ML for Physics: Innovative applications of machine learning to the physical sciences; Machine learning model interpretability for obtaining insights to physical systems; Automating/accelerating elements of the scientific process (experimental design, data collection, statistical analysis, etc.).
  • Physics in ML: Strategies for incorporating scientific knowledge or methods into machine learning models and algorithms; Applications of physical science methods and processes to understand, model, and improve machine learning models and algorithms.
  • Any other area related to the subject of the workshop, including but not limited to probabilistic methods that are relevant to physical systems, such as deep generative models, probabilistic programming, simulation-based inference, variational inference, causal inference, etc.

Dataset Track

We invite researchers to submit papers describing a dataset and the related computational or scientific challenge that may benefit from innovative research at the intersection of ML and Physical Sciences.
  • Availability of data set: The dataset must be publicly available at the time of the workshop (e.g., via Zenodo). The submission must also include a baseline result along with public code (the baseline need not use machine learning). Additional data artifacts (e.g. a public simulator) may also be included and described in the submission.
  • The submitted paper should describe the the following:
    • Properties of the dataset.
    • The scientific and/or computational challenges.
    • Existing methods and/or potential solutions that could be provided by ML.

Submissions should be anonymized short papers (extended abstracts) up to 4 pages in length (excluding references). We invite authors to follow the guidelines and best practices from the NeurIPS conference (see also the main conference datasets and benchmarks call for guidelines pertaining to the Dataset Track). Please ensure that your paper is approachable by someone who is not an expert in your specific area of physical science. For example, please avoid or at least define jargon. We reserve the right to desk reject any submissions that do not conform to the format. The review process is double blind. All authors must be registered in the submission system at the time of submission. We will not allow authors to be added after the review process has begun. This workshop is not archival so we will consider papers containing content that is published in an archival venue other than the main NeurIPS conference (e.g. a physics journal). However, such papers will likely need to be rewritten to fit the format and venue. See here for additional instructions on preparing submissions to the workshop.

Posters and contributed talks

Accepted work will be presented as posters during the workshop. At the same time as the in-person poster session, we will also facilitate a virtual poster session in GatherTown. Authors of submitted papers will be able to indicate their preference for an in-person presentation or a virtual presentation. Furthermore, the authors of each accepted paper will get the opportunity to submit a 5 minute video that summarizes their work.

Several accepted submissions will be selected for contributed talks at the workshop program. Talks can be in-person or remote depending on the preference of the presenter.

Submission instructions

Submit your work on the submission portal. See submission and review instructions for important instructions on preparing contributions as well as details on how they will be evaluated.

Submit paper

Review instructions

Instructions for reviewers are available here.

Review instruction

Important note for work that will be/has been published elsewhere

All accepted works will be made available on the workshop website. This does not constitute an archival publication or formal proceedings; authors retain full copyright of their work and are free to publish their extended work in another journal or conference. We allow submission of works that overlap with papers that are under review or have been recently published in a conference or a journal, including physical science journals. However, we do not accept cross-submissions of the same content to multiple workshops at NeurIPS. (Check the list of accepted workshops this year).

Instructions for accepted papers

Authors of accepted papers are expected to upload their camera-ready (final) paper and a poster by the deadlines given on this page. Optionally they can also record a short (5-minute) video describing their work.

Camera-ready paper

Please produce the "camera-ready" (final) version of your accepted paper by replacing the "neurips_2023.sty" style file with the "neurips_2023_ml4ps.sty" file available here and using the "final" package option (that is, "\usepackage[final]{neurips_2023_ml4ps}") to include author and affiliation information. The modified style file replaces the first-page footer to correctly refer to the workshop instead of the main conference. It is acceptable if your paper goes up to five pages (excluding acknowledgements, references, and any appendices if present) due to author and affiliation information taking extra space on the first page. The five-page limit is strict, and appendices are allowed but discouraged.

Please revise your paper as much as possible to address reviewer comments reasonably. The revision would include minor corrections and/or changes directly addressing reviewer comments. Beyond these points, it is not acceptable to have any significant new material not present in your paper's reviewed version. Please upload the final PDF of your paper by the camera-ready deadline by logging in to the CMT website (the same one used for the submissions) and using the camera-ready link shown with your existing submission.


Please upload your poster using the central NeurIPS poster upload page and follow the instructions given there regarding the file formats and resolutions. To see the poster listed in the NeurIPS poster upload page, the co-author who is uploading the poster for a paper needs to be logged in to the neurips.cc website using the same email address they used in their paper submission. If you encounter a problem regarding NeurIPS accounts (e.g., you have multiple accounts associated with different email addresses and you need to merge these accounts into a single one), please consult the NeurIPS account FAQs and get in touch with the main NeurIPS conference organization who are handling accounts and registrations.

The poster sessions will take place both in-person and virtually during the workshop.

  • Physical presentation: You must come with your poster printed, preferably on a lightweight paper of at most 24W x 36H inches. Your poster will be taped to the wall.
  • Remote presentation: Virtual poster sessions will be held online at the same time as the physical poster sessions. Further instructions will be sent later.

For the authors of contributed talks, posters are optional.

Optional video

You can record a short video in addition to your poster using a platform of your own choice (e.g., YouTube). Videos will be added to the workshop website, together with the papers and posters. The video should be a brief (less than 5 minutes) presentation of your work in the accepted paper. Uploading a video is optional. You should submit the URL of your presentation on CMT with the camera-ready version of your paper.

Important dates

  • Submission Deadline: September 29, 2023, 23:59 AoE
  • Review Deadline: October 21, 2023, 23:59 AoE
  • Author (accept/reject) notification: October 27, 2023, 23:59 AoE
  • Camera-ready (final) paper deadline: November 27, 2023, 23:59 AoE
  • Poster deadline: November 27, 2023, 23:59 AoE
  • Workshop: December 15, 2023


For questions and comments, please contact us at ml4ps@googlegroups.com.

Steering Committee

Team Members


Sponsors are welcome. Please contact us.


NeurIPS 2023 will be a hybrid conference with physical and virtual participation. The physical component will take place at the New Orleans Ernest N. Morial Convention Center, 900 Convention Center Blvd, New Orleans, LA 70130, United States