Machine Learning and the Physical Sciences, NeurIPS 2024

About

The Machine Learning and the Physical Sciences workshop aims to provide an informal, inclusive, and leading-edge venue for discussing research and challenges at the intersection of machine learning (ML) and the physical sciences (PS). This includes the applications of ML to problems in the physical sciences (ML for PS) as well as developments in ML motivated by physical insights (PS for ML).

Physical sciences are defined inclusively, including but not limited to physics, astronomy, cosmology, chemistry, biophysics, materials science, and Earth science.

Recent years have highlighted unique opportunities as well as challenges in incorporating ML workflows as part of the scientific process in many physical sciences. For example, fields focused on fundamental physics discovery, such as particle physics and cosmology, often have stringent requirements for exactness, robustness, and latency that go beyond those typically encountered in other scientific domains and industry applications. Data preservation and workflow reproducibility are other central challenges that need to be addressed in the era of large experiments, collaborations, and datasets. In these fields and others, simulations play a central role in connecting theoretical models to observations. The ubiquity and increasing complexity of simulators in PS has spurred methodological advances in ML, e.g. in simulation-based inference and differentiable programming, that are finding applications far beyond PS, showcasing the bidirectional nature of the PS-ML intersection.

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.

The invited talks program will showcase unique features of the physical sciences that highlight current challenges and bidirectional opportunities in ML and PS. This includes the central role of simulators in the scientific process, the need for rigorous uncertainty quantification, and the development of hardware-software co-design solutions for real-time inference.

A part of the workshop program will be dedicated to the focus area discussing the role of data-driven vs inductive bias-driven methods in machine learning and the physical sciences, centering the emerging role of foundation models and their complementarity with approaches leveraging physical inductive biases. This will feature an overview talk, followed by a moderated panel discussion.

NeurIPS 2024

The Machine Learning and the Physical Sciences 2024 workshop will be held on December 15, 2024 at the Vancouver Convention Center in Vancouver, BC, Canada as a part of the 38th annual conference on Neural Information Processing Systems (NeurIPS). The workshop is planned to take place in a hybrid format inclusive of virtual participation.

Speakers

Thea Aarrestad
ETH Zurich
Yasaman Bahri
Google DeepMind
Lukas Heinrich
Technical University of Munich

Ann Lee
Carnegie Mellon University
Laurence Perreault-Levasseur
Université de Montréal / Mila

Panelists

Topic: Data-driven vs inductive bias-driven methods in machine learning and the physical sciences.

Anima Anandkumar
Caltech
Johannes Brandstetter
JKU Linz / NXAI
Naoya Takeishi
University of Tokyo / RIKEN

Papers

Accepter papers are listed below; papers and posters will be linked after the camera-ready deadline.

2	Neural Infalling Clouds: Increasing the Efficacy of Subgrid Models and Scientific Equation Discovery using Neural ODEs and Symbolic Regression Brent Tan
4	Meta-Learned Bayesian Optimization for Energy Yield in Inertial Confinement Fusion Vineet Gundecha; Ricardo Luna Gutierrez; Sahand Ghorbanpour; Desik Rengarajan; Rahman Ejaz; Varchas Gopalaswamy; Riccardo Betti; Soumyendu Sarkar
5	Uncertainty-Penalized Bayesian Information Criterion for Parametric Partial Differential Equation Discovery Pongpisit Thanasutives; Ken-ichi Fukui
6	Multimodal multi-output ordinal regression for discovering gravitationally-lensed transients Nicolò Oreste Pinciroli Vago; Piero Fraternali
8	LLM Enhanced Bayesian Optimization for Scientific Applications like Fusion Sahand Ghorbanpour; Ricardo Luna Gutierrez; Vineet Gundecha; Desik Rengarajan; Ashwin Ramesh Babu; Soumyendu Sarkar
9	Normalising Flow for Joint Cosmological Analysis Arrykrishna Mootoovaloo; David Alonso; Jaime Ruiz-Zapatero; Carlos Garcia-Garcia
10	Emulation and Assessment of Gradient-Based Samplers in Cosmology Arrykrishna Mootoovaloo; David Alonso; Jaime Ruiz-Zapatero; Carlos Garcia-Garcia
11	Two-Stage Coefficient Estimation in Symbolic Regression for Scientific Discovery Masahiro Negishi; Yoshitomo Matsubara; Naoya Chiba; Ryo Igarashi; Yoshitaka Ushiku
13	Path-minimizing Latent ODEs as Inference Models Matt L. Sampson; Peter Melchior
14	Climate PAL: Climate Analysis through Conversational AI Sonia Cromp; Behrad Rabiei; Maxwell T. Elling; Alexander J. Herron; Michael Hendrickson
16	Physics-guided Optimization of Photonic Structures using Denoising Diffusion Probabilistic Models Dongjin Seo; Soobin Um; Sangbin Lee; Jong Chul Ye; Haejun Chung
17	Galaxy Formation and Evolution via Phase-temporal Clustering with FuzzyCat $\circ$ AstroLink William H. Oliver; Tobias Buck
18	Constrained Synthesis with Projected Diffusion Models Jacob K Christopher; Stephen Baek; Ferdinando Fioretto
19	ClariPhy: Physics-Informed Image Deblurring with Transformers for Hydrodynamic Instability Analysis Shai Stamler-Grossman; Nadav Schneider; Gershon Hanoch; Gal Oren
20	Evidential deep learning for probabilistic modelling of extreme storm events Ayush Khot; Xihaier Luo; Ai Kagawa; Shinjae Yoo
21	Learning Fluid-Directed Rigid Body Control Karlis Freivalds; Oskars Teikmanis; Laura Leja; Saltanovs Rodions; Ralfs Āboliņš
22	Galaxy Morphology Classification with Counterfactual Explanation Zhuo Cao; Lena Krieger; Hanno Scharr; Ira Assent
23	Dyson Brownian motion and random matrix dynamics of weight matrices during learning Gert Aarts; Ouraman Hajizadeh; Biagio Lucini; Chanju Park
24	Towards Using Large Language Models and Deep Reinforcement Learning for Inertial Fusion Energy Vadim Elisseev; Massimiliano Esposito; James C Sexton
25	A neural surrogate solver for radiation transfer Aleksei Sorokin; Xiaoyi Lu; Yi Wang
26	Improving Flow Matching for Simulation-Based Inference Janis Fluri; Thomas Hofmann
28	Automated discovery of large-scale, noise-robust experimental designs in super-resolution microscopy Carla Rodríguez; Sören Arlt; Leonhard Möckl; Mario Krenn
30	Neural Network Simulation of Time-variant Waves on Arbitrary Grids with Applications in Active Sonar Yash Ranjith
32	Efficient Generation of Molecular Clusters with Dual-Scale Equivariant Flow Matching Akshay Subramanian; Shuhui Qu; Cheol Woo Park; Sulin Liu; Janghwan Lee; Rafael Gomez-Bombarelli
33	Hybrid Prior Wavelet Flow For Extragalactic Foreground Simulations Matiwos Mebratu; W.L. Kimmy Wu
34	Scalable physics-guided data-driven component model reduction for steady Navier-Stokes flow Seung Whan Chung; Youngsoo Choi; Pratanu Roy; Thomas Roy; Tiras Lin; Du Nguyen; Christopher Hahn; Eric Duoss; Sarah Baker
37	From particle clouds to tokens: building foundation models for particle physics Joschka Birk; Anna Hallin; Gregor Kasieczka
39	Domain Adaptation of Drag Reduction Policy to Partial Measurements Anton Plaksin; Georgios Rigas
40	Reconstructing dissipative dynamical systems from spatially and temporally sparse sensors Alex Guo; Galen T. Craven; Javier E. Santos; Charles D. Young
41	BNEM: A Boltzmann Sampler Based on Bootstrapped Noised Energy Matching RuiKang OuYang; Bo Qiang; José Miguel Hernández-Lobato
42	The State of Julia for Scientific Machine Learning Edward Berman; Jacob Ginesin
43	AP-SVM: Unsupervised Data Cleaning for the LEGEND Experiment Esteban León; Julieta Gruszko; Aobo Li; Brady Bos; M.A. Bahena Schott; John Wilkerson; Reyco Henning; Matthew Busch; Eric L. Martin; Guadalupe Duran; J.R. Chapman
44	ChemLit-QA: A human evaluated dataset for chemistry RAG tasks Geemi Wellawatte; Philippe Schwaller; Huixuan Guo; Marta Brucka; Anna Borisova; Matthew Hart; Magdalena Lederbauer
45	GraphNeT 2.0 - A Deep Learning Library for Neutrino Telescopes Rasmus F. Ørsøe; Aske Rosted
46	Towards Agentic AI on Particle Accelerators Antonin Sulc; Thorsten Hellert; Raimund Kammering; Hayden R. Hoschouer; Jason M. St. John
47	A Poisson-process AutoDecoder for Astrophysical, Time-variable, X-ray Sources Yanke Song; V Ashley Villar; Juan Rafael Martínez-Galarza
48	A method for identifying causality in the response of nonlinear dynamical systems Joseph Massingham; Ole Mattis Nielsen; T Butlin
50	Meta-Designing Quantum Experiments with Language Models Sören Arlt; Haonan Duan; Felix Li; Sang Michael Xie; Yuhuai Wu; Mario Krenn
52	Enhancing Cosmological Simulations with Efficient and Interpretable Machine Learning in the Gabor Wavelet Basis Cooper Jacobus; Leander Thiele; Peter Harrington; Jia Liu; Zarija Lukic
53	A machine learning approach to duality in statistical physics Prateek Gupta; Andrea E. V. Ferrari; Nabil Iqbal
54	Synax: A Differentiable and GPU-accelerated Synchrotron Simulation Package Kangning Diao; Zack Li; Richard D.P. Grumitt; Yi Mao
56	Explicit and data-Efficient Encoding via Gradient Flow Kyriakos Flouris; Anna Volokitin; Gustav Bredell; Ender Konukoglu
58	Neural network prediction of strong lensing systems with domain adaptation and uncertainty quantification Shrihan Agarwal; Aleksandra Ciprijanovic; Brian Nord
59	Generation and Human-Expert Evaluation of Interesting Research Ideas using Knowledge Graphs and Large Language Models Xuemei Gu; Mario Krenn
60	Physics-informed Discovery of State Variables in Second-Order and Hamiltonian Systems Félix Chavelli; Zi-Yu Khoo; Dawen Wu; Jonathan Sze Choong Low; Stéphane Bressan
61	Neural 3D Reconstruction of 21-cm Tomographic Data Nashwan Sabti; Ram Purandhar Reddy Sudha; Julian B. Muñoz; Siddharth Mishra-Sharma; Taewook Youn
62	Machine learned reconstruction of tsunami waves from sparse observations Edward McDugald; Darren Engwirda; Arvind Mohan; Agnese Marcato; Javier E. Santos
65	Toward Model-Agnostic Detection of New Physics Using Data-Driven Signal Regions Soheun Yi; John Alison; Mikael Kuusela
66	Learning Pore-scale Multi-phase Flow from Experimental Data with Graph Neural Network Yuxuan Gu; Catherine Spurin; Gege Wen
67	Harnessing Loss Decomposition for Long-Horizon Wave Predictions via Deep Neural Networks Indu Kant Deo; Rajeev K. Jaiman
68	Scalable nonlinear manifold reduced order model for dynamical systems Ivan Zanardi; Alejandro N. Diaz; Seung Whan Chung; Marco Panesi; Youngsoo Choi
69	CODES: Benchmarking Coupled ODE Surrogates Robin Janssen; Immanuel Sulzer; Tobias Buck
70	Transfer Learning in Materials Informatics: structure-property relationships through minimal but highly informative multimodal input Dario Massa; Grzegorz Kaszuba; Stefanos Papanikolaou; Piotr Sankowski
71	Higher-order cumulants in diffusion models Gert Aarts; Diaa Eddin Habibi; Lingxiao Wang; Kai Zhou
72	Learning functional forms of fragmentation functions for hadron production using symbolic regression Nour Makke; Sanjay Chawla
73	Interpreting Multi-band Galaxy Observations with Large Language Model-Based Agents Zechang Sun; Yuan-Sen Ting; Yaobo Liang; Nan Duan; Song Huang; Zheng Cai
74	Training Hamiltonian neural networks without backpropagation Atamert Rahma; Chinmay Datar; Felix Dietrich
75	Reconstructing micro-magnetic vector fields based on topological charge distributions via generative neural network systems Kyra H. M. Klos; Jan Disselhoff; Karin Everschor-Sitte; Friederike Schmid
76	PICL: Learning to Incorporate Physical Information When Only Coarse-Grained Data is Available Haodong Feng; Yue Wang; Dixia Fan
77	Fast GPU-Powered and Auto-Differentiable Forward Modeling of IFU Data Cubes Ufuk Çakır; Anna Lena Schaible; Tobias Buck
78	LensPINN: Physics Informed Neural Network for Learning Dark Matter Morphology in Lensing Ashutosh Ojha; Sergei Gleyzer; Michael W. Toomey; Pranath Reddy
79	Deep Learning Based Superconductivity Prediction and Experimental Tests Daniel Kaplan; Adam Zheng; Joanna Blawat; Rongying Jin; Viktor Oudovenko; Gabriel Kotliar; Weiwei Xie; Anirvan M. Sengupta
81	Diffusion models for lattice gauge field simulations Qianteng Zhu; Gert Aarts; Wei Wang; Kai Zhou; Lingxiao Wang
83	First High-Resolution Galaxy Simulations Accelerated by a 3D Surrogate Model for Supernovae Keiya Hirashima; Kana Moriwaki; Michiko S. Fujii; Yutaka Hirai; Takayuki R. Saitoh; Junichiro Makino; Ulrich Philipp Steinwandel; Shirley Ho
84	Inferring Stability Properties of Chaotic Systems on Autoencoders’ Latent Spaces Elise Özalp; Luca Magri
86	PhysBERT: A Text Embedding Model for Physics Scientific Literature Thorsten Hellert; Andrea Pollastro; João Montenegro
88	Cosmological super-resolution of the 21-cm signal Simon Pochinda; Jiten Dhandha; Anastasia Fialkov; Eloy de Lera Acedo
89	DiffLense: A Conditional Diffusion Model for Super-Resolution of Gravitational Lensing Data Pranath Reddy; Michael W. Toomey; Hanna Parul; Sergei Gleyzer
90	Accelerated Bayesian parameter estimation and model selection for gravitational waves with normalizing flows Alicja Polanska; Thibeau Wouters; Peter Tsun Ho Pang; Kaze W. K. Wong; Jason McEwen
91	Gaussian Processes for Probabilistic Estimates of Earthquake Ground Shaking: A 1-D Proof-of-Concept Sam A. Scivier; Tarje Nissen-Meyer; Paula Koelemeijer; Atilim Gunes Baydin
93	Efficient and Unbiased Sampling of Boltzmann Distributions via Consistency Models Fengzhe Zhang; Jiajun He; Laurence Illing Midgley; Javier Antoran; José Miguel Hernández-Lobato
94	PCN: a deep learning approach to jet tagging utilizing novel graph construction methods and Chebyshev graph convolutions Mihir Relan; Yash Semlani; Krithik Ramesh
96	Embedding Theoretical Baselines For Satellite Force Estimations Benjamin Y. J. Wong; Sai Sudha Ramesh; Khoo Boo Cheong
97	DYffCast: Regional Precipitation Nowcasting Using IMERG Satellite Data. A case study over South America Daniel Seal; Rossella Arcucci; Salva Rühling Cachay; César Quilodrán-Casas
101	D3PU: Denoising Diffusion Detector Probabilistic Unfolding in High-Energy Physics Camila Pazos; Shuchin Aeron; Pierre-Hugues Beauchemin; Vincent Croft; Martin Klassen; Taritree Wongjirad
102	CASBI – Chemical Abundance Simulation-Based Inference for Galactic Archeology Giuseppe Viterbo; Tobias Buck
103	Neural rendering enables dynamic tomography Ivan Grega; William F Whitney; Vikram Deshpande
104	Evaluating Sparse Galaxy Simulations via Out-of-Distribution Detection and Amortized Bayesian Model Comparison Lingyi Zhou; Stefan T. Radev; William H. Oliver; Aura Obreja; Zehao Jin; Tobias Buck
107	Domain adaptation in application to gravitational lens finding Hanna Parul; Michael W. Toomey; Pranath Reddy; Sergei Gleyzer
108	TELD: Trajectory-Level Langevin Dynamics for Versatile Constrained Sampling Magnus Petersen; Gemma Roig; Roberto Covino
109	Dynamic Curriculum Regularization for Enhanced Training of Physics-Informed Neural Networks Callum Duffy; Gergana V. Velikova
110	Semi-supervised Super-resolution for Gravitational Lenses with Estimated Degradation Model Peimeng Guan; Michael W. Toomey; Sergei Gleyzer
111	Using transfer learning to improve the generalization of machine learning models for photometric redshift estimation Jonathan Soriano; Srinath Saikrishnan; Vikram Seenivasan; Bernie Boscoe; Jack Singal; Tuan Do
112	Can KANs (re)discover predictive models for Direct-Drive Laser Fusion? Rahman Ejaz; Varchas Gopalaswamy; Aarne Lees; Riccardo Betti; Christopher Kanan
113	Uncertainty Quantification for Martian Surface Spectral Analysis using Bayesian Deep Learning Mark Hinds; Michael Geyer; Natalie Klein
114	MRI Parameters Mapping via Variational Inference Moucheng Xu; Yukun Zhou; Tobias Goodwin-Allcock; Kimia Firoozabadi; Joseph Jacob; Daniel C. Alexander; Paddy J. Slator
115	Evolutionary and Transformer based methods for Symbolic Regression Samyak Jha; Sergei Gleyzer; Eric A. F. Reinhardt; Victor Baules; Francois Charton; Nobuchika Okada
117	MATEY: multiscale adaptive foundation models for spatiotemporal physical systems Pei Zhang; M. Paul Laiu; Matthew R Norman; Doug Stefanski; John Gounley
118	S-KANformer: Enhancing Transformers for Symbolic Calculations in High Energy Physics Ritesh Bhalerao; Eric A. F. Reinhardt; Sergei Gleyzer; Nobuchika Okada; Victor Baules
119	Deep Multimodal Representation Learning for Stellar Spectra Tobias Buck; Christian Schwarz
120	History-Matching of Imbibition Flow in Multiscale Fractured Porous Media Using Physics-Informed Neural Networks (PINNs) Jassem Abbasi; Ben Moseley; Takeshi Kurotori; Ameya D. Jagtap; Anthony Kovscek; Aksel Hiorth; Pål Østebø Andersen
121	Domain-Adaptive ML for Surface Roughness Predictions in Nuclear Fusion Shashank Galla; Antonios Alexos; Jay Phil Yoo; Junze Liu; Kshitij Bhardwaj; Sean Hayes; Monika Biener; Pierre Baldi; Satish Bukkapatnam; Suhas Bhandarkar
122	Estimating Dark Matter Halo Masses in Simulated Galaxy Clusters with Graph Neural Networks Nikhil Garuda; John F Wu; Dylan Nelson; Annalisa Pillepich
123	DeepUQ: A Systematic Comparison of Aleatoric Uncertainties from Deep Learning Methods Rebecca Nevin; Brian Nord; Aleksandra Ciprijanovic
124	Unsupervised Physics-Informed Super-Resolution of Strong Lensing Images for Sparse Datasets Anirudh Shankar; Michael W. Toomey; Sergei Gleyzer
125	Integrating Generative and Physics-Based Models for Ptychographic Imaging with Uncertainty Quantification Canberk Ekmekci; Tekin Bicer; Zichao (Wendy) Di; Junjing Deng; Mujdat Cetin
127	Video-Driven Graph Network-Based Simulators Franciszek Szewczyk; Gilles Louppe; Matthia Sabatelli
128	Taylor Mode Neural Operators: Enhancing Computational Efficiency in Physics-Informed Neural Operators Anas Jnini; Flavio Vella
130	Neural Embeddings Evolve as Interacting Particles Rohan Mehta; Ziming Liu; Max Tegmark
131	Point cloud diffusion models for the Electron-Ion Collider Fernando Torales Acosta; Vinicius Mikuni; Felix Ringer; Nobuo Sato; Richard Whitehill
133	Galaxy Dust Maps with Conditional Score Models Jared Siegel; Peter Melchior
134	A perspective on symbolic machine learning in physical sciences Nour Makke; Sanjay Chawla
135	Physics-informed reduced order model with conditional neural fields Minji Kim; Tianshu Wen; Kookjin Lee; Youngsoo Choi
136	Digital Discovery of interferometric Gravitational Wave Detectors Mario Krenn; Yehonathan Drori; Rana Adhikari
137	Geometry-aware PINNs for Turbulent Flow Prediction Shinjan Ghosh; Julian Busch; Georgia Olympia Brikis; Biswadip Dey
138	Neural Entropy Akhil Premkumar
139	Learning the Evolution of Physical Structure of Galaxies via Diffusion Models Andrew Lizarraga; Eric Hanchen Jiang; Jacob Nowack; Yun Qi Li; Ying Nian Wu; Bernie Boscoe; Tuan Do
140	FB-HyDON: Parameter-Efficient Physics-Informed Operator Learning of Complex PDEs via Hypernetwork and Finite Basis Domain Decomposition Milad Ramezankhani; Rishi Yash Parekh; Anirudh Deodhar; Dagnachew Birru
141	Towards Commercialization of Tokamaks: Time Series Viewmakers for Robust Disruption Prediction Dhruva Chayapathy; Tavis Siebert; Akshata Kishore Moharir; Lucas Spangher; Om Manoj Patil; Cristina Rea
142	Super-Resolution without High-Resolution label for Black Hole Simulations Thomas Helfer; Thomas Edwards; Jessica Dafflon; Kaze W. K. Wong; Matthew Lyle Olson
143	Reinforcement Learning for Control of Non-Markovian Cellular Population Dynamics Josiah C Kratz; Jacob Adamczyk
145	Explainable Deep Learning Framework for SERS Bio-quantification Jihan K. Zaki; Jakub Tomasik; Sabine Bahn; Jade A. McCune; Pietro Lio; Oren A. Scherman
146	Learning dictionaries of New Physics with sparse local kernels Gaia Grosso; Philip Harris; Ekaterina Govorkova; Eric A. Moreno; Ryan Raikman
148	Multi-Wavelength Analysis of Kilonova Associated with GRB 230307A: Accelerated Parameter Estimation and Model Selection Through Likelihood-Free Inference P. Darc; Clecio R. De Bom; Gabriel S. M. Teixeira; Charles Kilpatrick; Nora F. Sherman; Marcelo P. Albuquerque; Paulo Russano
150	Multidimensional Deconvolution with Profiling Huanbiao Zhu; Mikael Kuusela; Larry Wasserman; Benjamin Nachman; Krish Desai; Vinicius Mikuni
151	A Physics-Informed Autoencoder-NeuralODE Framework (Phy-ChemNODE) for Learning Complex Fuel Combustion Kinetics Tadbhagya Kumar; Pinaki Pal; Anuj Kumar
152	AICircuit: A Multi-Level Dataset and Benchmark for AI-Driven Analog Integrated Circuit Design Asal Mehradfar; Xuzhe Zhao; Yue Niu; Sara Babakniya; Mahdi Alesheikh; Hamidreza Aghasi; Salman Avestimehr
153	Real-time Position Reconstruction for the KamLAND-Zen Experiment using Hardware-AI Co-design Alexander Migala; Eugene Ku; Zepeng Li; Aobo Li
154	Randomized reward redistribution for HPGe waveform classification under weakly-supervised learning setup Sonata Simonaitis-Boyd; Aobo Li
156	Systematic Uncertainties and Data Complexity in Normalizing Flows Sandip Roy; Yonatan Kahn; Jessie Shelton; Victoria Tiki
157	Exact and approximate error bounds for physics-informed neural networks Augusto T. Chantada; Pavlos Protopapas; Luca J. Gomez Bachar; Susana J. Landau; Claudia G. Scóccola
158	Machine Learning for Reparameterization of Multi-scale Closures Hilary Egan; peter ciecielski; hariswaram sitaraman; megan crowley
159	Uncertainty Quantification From Scaling Laws in Deep Neural Networks Ibrahim Elsharkawy; Yonatan Kahn; Benjamin Hooberman
160	Reconstruction of Continuous Cosmological Fields from Discrete Tracers with Graph Neural Networks Yurii Kvasiuk; Jordan Krywonos; Matthew C. Johnson; Moritz Münchmeyer
161	Similarity-Aware Relative Difference Learning for Improved Molecular Activity Prediction Karina Zadorozhny; Kangway V. Chuang; Bharath Sathappan; Ewan Wallace; Vishnu Sresht; Colin A Grambow
162	Joint cosmological parameter inference and initial condition reconstruction with Stochastic Interpolants Carolina Cuesta-Lazaro; Chirag Modi; Siddharth Mishra-Sharma; Michael Samuel Albergo; Adrian E. Bayer; Daniel J. Eisenstein
163	Product Manifold Machine Learning for Physics Nathaniel S. Woodward; Sang Eon Park; Gaia Grosso; Jeffrey Krupa; Philip Harris
164	Reconstructing Quasar Spectra and Measuring the Ly$\alpha$ Forest with {\sc SpenderQ} ChangHoon Hahn; Satya Gontcho A Gontcho; Peter Melchior
165	Equation-driven Neural Networks for Periodic Quantum Systems Circe Hsu; Marios Mattheakis; Gabriel R Schleder; Daniel T. Larson
166	Physics-based Differentiable X-ray Rendering Improves Unsupervised 3D CBCT Reconstruction Mohammadhossein Momeni; Vivek Gopalakrishnan; Neel Dey; Polina Golland; Sarah Frisken
167	GFlowNets for Hamiltonian decomposition in groups of compatible operators Rodrigo Vargas-Hernandez; Isaac L. Huidobro-Meezs; Jun Dai; Guillaume Rabusseau
168	Symbolic regression for precision LHC physics Manuel Morales-Alvarado; Josh Bendavid; Daniel Conde; Veronica Sanz; Maria Ubiali
169	An end-to-end generative model for heavy-ion collisions Jing-An Sun
170	Using Variational Autoencoding to Infer the Masses of Exoplanets Embedded in the Disks of Gas and Dust Orbiting Young Stars Sayed Shafaat Mahmud; Ramit Dey; Sayantan Auddy; Neal Turner; Jeffrey Bary
171	Neural Networks for Dissipative Physics Using Morse-Feshbach Lagrangian Veera Sundararaghavan; Jeff Simmons; Megna Shah
175	Transforming Simulation to Data Without Pairing Eli Gendreau-Distler; Luc Tomas Le Pottier; Haichen Wang
176	Robust Emulator for Compressible Navier-Stokes using Equivariant Geometric Convolutions Wilson G. Gregory; David W Hogg; Kaze W. K. Wong; Soledad Villar
177	Neural Posterior Unfolding Jingjing Pan; Benjamin Nachman; Vinicius Mikuni; Jay Chan; Krish Desai; Fernando Torales Acosta
178	Uncertainty Quantification for Surface Ozone Emulators using Deep Learning Kelsey Doerksen; Yuliya Marchetti; James Montgomery; Yarin Gal; Freddie Kalaitzis; Kazuyuki Miyazaki; Kevin Bowman; Steven Lu
180	AI Meets Antimatter: Unveiling Antihydrogen Annihilations Ashley Ferreira; Mahip Singh; Andrea Capra; Ina Carli; Daniel Duque Quiceno; Wojciech T. Fedorko; Makoto Fujiwara; Muyan Li; Lars Martin; Yukiya Saito; Gareth Smith; Anqi Xu
181	Correcting misspecified score-based priors for inverse problems: An application to strong gravitational lensing Gabriel Missael Barco; Alexandre Adam; Connor Stone; Yashar Hezaveh; Laurence Perreault-Levasseur
182	Data-Driven, Parameterized Reduced-order Models for Predicting Distortion in Metal 3D Printing Indu Kant Deo; Youngsoo Choi; Saad Khairallah; Alexandre Reikher; Maria Strantza
183	Which bits went where? Past and future transfer entropy decomposition with the information bottleneck Kieran A. Murphy; Zhuowen Yin; Danielle Bassett
185	Variational Loss Landscapes for Periodic Orbits Leo Yao; Ziming Liu; Max Tegmark
186	Live Constrained Deep Learning Models Optimize Unmanned Underwater Vehicle Control Systems Brian Lee Zhou; Kamal Viswanath; Jason Geder
188	Amortizing intractable inference in diffusion models for Bayesian inverse problems Siddarth Venkatraman; Moksh Jain; Luca Scimeca; Minsu Kim; Marcin Sendera; Mohsin Hasan; Luke Rowe; Sarthak Mittal; Pablo Lemos; Emmanuel Bengio; Alexandre Adam; Jarrid Rector-Brooks; Yashar Hezaveh; Laurence Perreault-Levasseur; Yoshua Bengio; Glen Berseth; Nikolay Malkin
189	Interpreting Transformers for Jet Tagging Aaron Wang; Abhijith Gandrakota; Elham E Khoda; Vivekanand Gyanchand Sahu; Javier Duarte; Priyansh Bhatnagar; Jennifer Ngadiuba
190	Learning Conformal Field Theory with Symbolic Regression: Recovering the Symbolic Expressions for the Energy Spectrum Haotian Cao; Garrett W. Merz; Kyle Cranmer; Gary Shiu
191	Bumblebee: Foundational Model for Particle Physics Discovery Andrew J. Wildridge; Jack P. Rodgers; Mia Liu; Yao yao; Andreas W. Jung; Ethan M. Colbert
193	Robust one-shot spectroscopic multi-component gas mixture detection via randomized smoothing Mohamed Sy; Emad Al Ibrahim; Aamir Farooq
194	Conditional Diffusion Models for Generating Images of SDSS-Like Galaxies Mikaeel Yunus; John F Wu; Timothy Heckman; Benne W Holwerda
198	Dissipativity-Informed Learning for Chaotic Dynamical Systems with Attractor Characterization Sunbochen Tang; Themistoklis Sapsis; Navid Azizan
199	No Location Left Behind: Introducing the Fairness Assessment for Implicit Representations of Earth Data Daniel Cai; Randall Balestriero
200	GeoWavelets: Spherical Wavelets for Fair Implicit Representations of Earth Data Daniel Cai; Randall Balestriero
201	Graph rewiring for long range-aware protein learning Ali Hariri; Pierre Vandergheynst
202	Unpaired Translation of Point Clouds for Modeling Detector Response Mingyang Li; Curtis Hunt; Michelle P. Kuchera; Raghuram Ramanujan; Yassid Ayyad; Adam K. Anthony
203	Convolutional Vision Transformer for Cosmology Parameter Inference Yash Gondhalekar; Kana Moriwaki
204	Zephyr quantum-assisted hierarchical Calo4pQVAE for particle-calorimeter interactions Ian Lu; Hao Jia; Sebastian Gonzalez; Deniz Sogutlu; Javier Toledo; Sehmimul Hoque; Abhishek Abhishek; Colin Gay; Roger Melko; Eric Paquet; Geoffrey Fox; Maximilian Swiatlowski; Wojciech T. Fedorko
206	Generation of Air Shower Images for Imaging Air Cherenkov Telescopes using Diffusion Models Christian Elflein; Stefan Funk; Jonas Glombitza; Vinicius Mikuni; Benjamin Nachman; Lark Wang
207	WOTAN: Weakly-supervised Optimal Transport Attention-based Noise Mitigation Nathan Suri; Vinicius Mikuni; Benjamin Nachman
208	Discovering How Ice Crystals Grow Using NODE's and Symbolic Regression Kara D Lamb; Jerry Harrington
209	Learning Locally Adaptive Metrics that Enhance Structural Representation with LAMINAR Christian Kleiber; William H. Oliver; Tobias Buck
211	Virtual Reality for Understanding Artificial-Intelligence-driven Scientific Discovery with an Application in Quantum Optics Philipp Schmidt; Carlos Ruiz-Gonzalez; Sören Arlt; Xuemei Gu; Carla Rodríguez; Mario Krenn
212	Clifford Flows Francesco Alesiani; Takashi Maruyama
214	OrbNet-Spin: Quantum Mechanics Informed Geometric Deep Learning For Open-shell Systems Beom Seok Kang; Mohammadamin Tavakoli; Vignesh C Bhethanabotla; William Goddard; Anima Anandkumar
215	Bayesian Deconvolution of Astronomical Images with Diffusion Models: Quantifying Prior-Driven Features in Reconstructions Alessio Spagnoletti; Marc Huertas-Company; Alexandre Boucaud; Wassim Kabalan; Biswajit Biswas
216	Topological data analysis of large swarming dynamics Yoh-ichi Mototake; Shinichi Ishida; Norihiro Maruyama; Takashi Ikegami
217	Shaping Flames with Differentiable Physics Simulations Laura Leja; Karlis Freivalds; Oskars Teikmanis
220	Learning Physics From Video: Unsupervised Physical Parameter Estimation for Dynamical Systems Alejandro Castañeda Garcia; Jan van Gemert; Daan Brinks; Nergis Tomen
221	Flow Annealed Importance Sampling Bootstrap meets Differentiable Particle Physics Annalena Kofler; Vincent Stimper; Mikhail Mikhasenko; Michael Kagan; Lukas Heinrich
222	Learning Symmetry-Independent Jet Representations via Jet-Based Joint Embedding Predictive Architecture Subash Katel; Haoyang Li; Zihan Zhao; Javier Duarte
224	Hybrid Summary Statistics T. Lucas Makinen; Ce Sui; Benjamin Dan Wandelt
225	Testing Uncertainty of Large Language Models for Physics Knowledge and Reasoning Elizaveta Reganova; Peter Steinbach
226	Uncertainty quantification for fast reconstruction methods using augmented equivariant bootstrap: Application to radio interferometry Mostafa Cherif; Tobías I. Liaudat; Jonathan Kern; Christophe Kervazo; Jerome Bobin
227	Loss function to optimise signal significance in particle physics Jai Bardhan; Cyrin Neeraj; Subhadip Mitra; Tanumoy Mandal
228	Toward fast galaxy catalog generation with diffusion models Tanner Sether; Elena Giusarma; Mauricio Reyes
230	Unlocking Ion-Scale Coherent Structures in the Solar Wind with Machine Learning Yufei Yang
231	3D-PDR Orion dataset and NeuralPDR: Neural Ordinary Equations for Photodissociation regions Gijs Vermariën; Serena Viti; Rahul Ravichandran; Thomas G. Bisbas
234	A Platform, Dataset, and Challenge for Uncertainty-Aware Machine Learning David Rousseau; Wahid Bhimji; Ragansu Chakkappai; Steven Farrell; Aishik Ghosh; Isabelle Guyon; Chris Harris; Elham E Khoda; Benjamin Nachman; Ihsan Ullah; Sascha Diefenbacher; Yuan-Tang Chou; Paolo Calafiura; Yulei Zheng; Jordan Dudley
235	Mean-Field Simulation-Based Inference for Cosmological Initial Conditions Oleg Savchenko; Florian List; Noemi Anau Montel; Christoph Weniger; Guillermo Franco Abellan
237	Towards long rollout of neural operators with local attention and flow matching-inspired correction: An example in frontal polymerization PDEs Pengfei Cai; Sulin Liu; Qibang Liu; Philippe Geubelle; Rafael Gomez-Bombarelli
239	Simulation-based inference with scattering representations: scattering is all you need Kiyam Lin; Benjamin Joachimi; Jason McEwen
240	CURIE: Evaluating LLMs on Multitask Scientific Long-Context Understanding and Reasoning Hao Cui; Zahra Shamsi; Xuejian Ma; Gowoon Cheon; Shutong Li; Maria Tikhanovskaya; Nayantara Mudur; Martyna Beata Plomecka; Peter Christian Norgaard; Paul Raccuglia; Victor V. Albert; Yasaman Bahri; Pranesh Srinivasan; Haining Pan; Philippe Faist; Brian A Rohr; Michael J. Statt; Dan Morris; Drew Purves; Elise Kleeman; Ruth Alcantara; Matthew Abraham; Muqthar Mohammad; Ean Phing VanLee; Chenfei Jiang; Elizabeth Dorfman; Eun-Ah Kim; Michael Brenner; Sameera S Ponda; Subhashini Venugopalan
241	Differentiable Conservative Radially Symmetric Fluid Simulations and Stellar Winds $\circ$ jf1uids Leonard Storcks; Tobias Buck
242	Port-Hamiltonian Neural Networks for Learning Coupled Systems and Their Interactions Razmik Arman Khosrovian; Takaharu Yaguchi; Takashi Matsubara
244	Harnessing Machine Learning for Single-Shot Measurement of Free Electron Laser Pulse Power Till Korten; Vladimir Rybnikov; Mathias Vogt; Juliane Roensch-Schulenburg; Peter Steinbach; Najmeh Mirian
246	Quantum Wasserstein Compilation: Unitary Compilation using the Quantum Earth Mover's Distance Marvin Richter; Abhishek Y. Dubey; Axel Plinge; Christopher Mutschler; Daniel Scherer; Michael Hartmann
247	RoBo6: Standardized MMT Light Curve Dataset for Rocket Body Classification Daniel Kyselica; Marek Suppa; Jiří Šilha; Roman Ďurikovič
248	fBm-Based Generative Inpainting for the Reconstruction of Chromosomal Distances Alexander Lobashev; Dmitry Guskov; Kirill Polovnikov
249	Enhancing Molecular Expressiveness through Multi-View Representations Indra Priyadarsini; Seiji Takeda; Lisa Hamada; Hajime Shinohara
251	SE(3) Equivariant Topologies for Structure-based Drug Discovery Alvaro Prat; Hisham Abdel Aty; Aurimas Pabrinkis; Orestis Bastas; Tanya Paquet; Gintautas Kamuntavičius; Roy Tal
252	Fine-tuning Foundation Models for Molecular Dynamics: A Data-Efficient Approach with Random Features Pietro Novelli; Luigi Bonati; Pedro J. Buigues; Giacomo Meanti; Lorenzo Rosasco; Michele Parrinello; massimiliano pontil
253	Diffusion-Based Inverse Solver on Function Spaces With Applications to PDEs Abbas Mammadov; Julius Berner; Kamyar Azizzadenesheli; Jong Chul Ye; Anima Anandkumar
254	PINNfluence: Influence Functions for Physics-Informed Neural Networks Jonas Naujoks; Aleksander Krasowski; Moritz Weckbecker; Thomas Wiegand; Sebastian Lapuschkin; Wojciech Samek; René Pascal Klausen
255	3D Cloud reconstruction through geospatially-aware Masked Autoencoders Stella Girtsou; Emiliano Diaz; Lilli Freischem; Joppe Massant; Kyriaki-Margarita Bintsi; Giuseppe Castiglione; William Jones; Michael Eisinger; Juan Emmanuel Johnson; Anna Jungbluth
257	Speak so a physicist can understand you! TetrisCNN for detecting phase transitions and order parameters Kacper Cybiński; James Enouen; Antoine Georges; Anna Dawid
258	Score-based models for 1/f correlated noise correction in James Webb Space Telescope spectral data Salma Salhi; Alexandre Adam; Loic Albert; Rene Doyon; Laurence Perreault-Levasseur
259	PolarBERT: A Foundation Model for IceCube Inar Timiryasov; Jean-Loup Tastet; Oleg Ruchayskiy
260	Open-Source Molecular Processing Pipeline for Generating Molecules Karan Bania; Shreyas V; Bharath Ramsundar; Jose Siguenza
261	Jrystal: A JAX-based Differentiable Density Functional Theory Framework for Materials Tianbo Li; Zekun Shi; Stephen Gregory Dale; Giovanni Vignale; Min Lin
262	Sharing Space: A Survey-agnostic Variational Autoencoder for Supernova Science Kaylee de Soto; Ana Sofia Uzsoy; V Ashley Villar
264	Diffusion-Based Inpainting of Corrupted Spectrogram Mahsa Massoud; Reyhane Askari-Hemmat; Kai-Feng Chen; Adrian Liu; Siamak Ravanbakhsh
266	Tomographic SAR Reconstruction for Forest Height Estimation Grace Beaney Colverd; Jumpei Takami; Laura Schade; Karol Bot; Joseph Alejandro Gallego Mejia
267	Hamiltonian Learning using Machine Learning Models Trained with Continuous Measurements Amit Kiran Rege; Kris Tucker; Conor Smith; Claire Monteleoni
269	Data-Driven Reweighting for Monte Carlo Simulations Ahmed Youssef; Christian Bierlich; Phil Ilten; Tony Menzo; Stephen Mrenna; Manuel Szewc; Michael K. Wilkinson; Jure Zupan
270	Towards a Reinforcement Learning framework for purely online 3D-molecular structure discovery Bjarke Hastrup; François R J Cornet; Tejs Vegge; Arghya Bhowmik

Program Committee (Reviewers)

We acknowledge the 333 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.

Abhijeet Parida (Childrens National Medical ), Abhijith Gandrakota (Fermi National Accelerator Laboratory (Fermilab)), Abhinanda Ranjit Punnakkal (University of Tromsø), Abhishek Abhishek (University of British Columbia), Abhishek Chandra (Eindhoven University of Technology), Abhishikth Mallampalli (University of Wisconsin - Madison), Adrian Perez-Suay (Universidad de Valencia), Agnimitra Dasgupta (University of Southern California), Ahmed MAZARI (Ansys, SimAI team), Ahmed Youssef (University of Cincinnati), Aidan Durr Chambers (Harvard University), Aizhan Akhmetzhanova (Harvard University, Harvard University), Alex Sun (University of Texas at Austin), Alexander Migala (University of California, San Diego), Alexander Thomas Gagliano (Massachusetts Institute of Technology), Alexandre Adam (Université de Montréal), Alexandre Strube (Forschungszentrum Juelich GmbH), Aman Desai (University of Adelaide), AmirEhsan Khorashadizadeh (University of Basel), Amit Kumar Jaiswal (University of Surrey), Anant Wairagade (IEEE Phoenix), Andrew Stevens (OptimalSensing), Andrey A Popov (University of Hawaii at Manoa), Ankita Shukla (University of Nevada, Reno), Anna Dawid (Leiden University, Leiden University), Anna Jungbluth (European Space Agency), Annalena Kofler (Max-Planck Institute), Antonin Sulc (Universität Konstanz), Arvind Mohan (Los Alamos National Laboratory), Arvind Ramanathan (Argonne National Laboratory), Arvind Renganathan (University of Minnesota - Twin Cities), Asal Mehradfar (University of Southern California), Athénaïs Gautier (McGill University), Atul Agrawal (Technische Universität München), Bariscan Kurtkaya (Stanford University), Benjamin Nachman (Lawrence Berkeley National Lab), Benjamin Y. J. Wong (National University of Singapore), Bharath Ramsundar (Deep Forest Sciences), Bilal Thonnam Thodi (New York University), Biprateep Dey (University of Pittsburgh), Biswarup Bhattacharya (Citadel), Biwei Dai (University of California Berkeley), Brian Nord (Fermi National Accelerator Laboratory), Bruno Raffin (INRIA), Carolina Cuesta-Lazaro (Massachusetts Institute of Technology), Cenk Tüysüz (DESY), Cheng Soon Ong (Australian National University), Chenyang Li (Argonne National Laboratory), Christine Allen-Blanchette (Princeton University), Christoph Weniger (University of Amsterdam), Christopher C. Hall (RadiaSoft LLC), Claire David (African Institute for Mathematical Sciences (South Africa)), Claudius Krause (HEPHY), Conrad M Albrecht (Columbia University), Constantin Weisser (Massachusetts Institute of Technology), Daniel Serino (Los Alamos National Laboratory), Danyal Rehman (Mila - Quebec Artificial Intelligence Institute), David Rousseau (IJCLab), Deep Chatterjee (Massachusetts Institute of Technology), Devesh Upadhyay (Saab ), Dimitra Maoutsa (Technische Universität München), Dongjin Seo (Yale University), Duccio Pappadopulo (Bloomberg), Edward Berman (Northeastern University), Elham E Khoda (University of California, San Diego), Eliane Maalouf (Université de Neuchâtel), Elise Özalp (Imperial College London), Elyssa Hofgard (Massachusetts Institute of Technology), Emanuele Usai (The University of Alabama), Engin Eren (Universität Hamburg), Enrico Rinaldi (Quantinuum), Eric Metodiev (Renaissance), Fabian Ruehle (Northeastern University), Fadoua Khmaissia (Bell Labs), Fatih Dinc (University of California, Santa Barbara), Favour Nerrise (Stanford University), Felix Wagner (ETHZ - ETH Zurich), Feng Chen (Stanford University), Fernando Torales Acosta (Lawrence Berkeley National Lab), Feyi Olalotiti (Intel), Francesco Alesiani (NEC), Francisco Villaescusa-Navarro (Princeton University), Franco Pellegrini (International School for Advanced Studies Trieste), Francois Lanusse (CNRS), François Gygi (University of California, Davis), François Rozet (Université de Liège), Gabriel Perdue (Fermi National Accelerator Laboratory), Gadi Naveh (GSK plc), Gaia Grosso (Massachusetts Institute of Technology), Gal Oren (Stanford University), Garrett W. Merz (University of Wisconsin - Madison), Gemma Zhang (Harvard University), George Stein (Layer6 AI), Georges Tod (University Paris Descartes), Gergana V. Velikova (PASQAL), Gert-Jan Both (HHMI Janelia Research Campus), Gijs Vermariën (Leiden Observatory, Leiden University), Gilles Louppe (University of Liège), Gregory Mermoud (HES-SO : UAS Western Switzerland), Gustau Camps-Valls (Universitat de València), H H C (University of California, Merced), Haimeng Zhao (California Institute of Technology), Haodong Feng (Westlake University), Haowei Ni (Johnson and Johnson), Haoxuan Chen (Stanford University), Haoyang Zheng (Purdue University), Harold Erbin (CEA), Henning Kirschenmann (University of Helsinki), Huichi Zhou (Imperial College London), Hunor Csala (University of Utah), Inbar Savoray (University of California, Berkeley), Indu Kant Deo (University of British Columbia), Irina Espejo Morales (International Business Machines), Ivan Grega (University of Cambridge), Jack Collins (Bosch), Jacob A Zwart (U.S. Geological Survey), Jacob Adamczyk (University of Massachusetts Boston), Jan Olle (Max Planck Institute for the Science of Light), Jason McEwen (University College London), Jay Chan (Lawrence Berkeley National Lab), Jay Taneja (University of Massachusetts at Amherst), Jean-roch Vlimant (California Institute of Technology), Jenna Pope (Pacific Northwest National Laboratory), Jeongwhan Choi (Yonsei University), Jesse Thaler (Massachusetts Institute of Technology), Jiahe Huang (University of Michigan - Ann Arbor), Jiajing Chen (New York University), Jianjun Hu (University of South Carolina), Jie Gao (Rutgers University), Jihan K. Zaki (University of Cambridge), Jingjing Pan (Yale University), Jingyi Tang (Stanford University), Jochen Garcke (University of Bonn), Joel Dabrowski (Data61, CSIRO), Johan de Kleer (c-infinity), John F Wu (Space Telescope Science Institute), Jordi Tura (Leiden University), Jose Francisco Ruiz-Munoz (Universidad Nacional de Colombia), Jose Manuel Napoles-Duarte (Universidad Autónoma de Chihuahua), Joseph Alejandro Gallego Mejia (Drexel University), Joshua Isaacson (Fermi National Accelerator Laboratory), Joshua Yao-Yu Lin (Prescient Design/ Genentech), Junichi Tanaka (International Center for Elementary Particle Physics, The University of Tokyo), Junze Liu (University of California, Irvine), Kai Fukami (University of California, Los Angeles), Karolos Potamianos (University of Oxford), Kartik Mathur (Microsoft), Katherine Fraser (University of California, Berkeley), Kathleen Champion (Amazon), Keith Brown (Boston University, Boston University), Keming Zhang (University of California Berkeley), Kieran A Murphy (University of Pennsylvania), Kim Andrea Nicoli (Rheinische Friedrich-Wilhelms Universität Bonn), Kiri Wagstaff (American Association for the Advancement of Science), Krish Desai (University of California, Berkeley), Kyongmin Yeo (International Business Machines), Kyriakos Flouris (Swiss Federal Institute of Technology), Lalit Ghule (Ansys Inc), Lars Doorenbos (Universität Bern), Leander Thiele (Princeton University), Line H Clemmensen (Technical University of Denmark), Lingxiao Wang (RIKEN), Lipi Gupta (Lawrence Berkeley National Lab), Luc Tomas Le Pottier (University of California, Berkeley), Lucas Thibaut Meyer (INRIA), Ludger Paehler (Technical University Munich), MD SAJID (Indian Institute of Technology Indore), MUHAMMAD AMIN NADIM (University of Pegaso), Madhurima Nath (Virginia Polytechnic Institute and State University), Mahsa Massoud (McGill University, McGill University), Mai H Nguyen (University of California, San Diego), Maithili Bhide (University of California, Los Angeles), Mallikarjuna Tupakula (Rochester Institute of Technology), Marco Letizia (University of Genoa), Maria Piles (Universidad de Valencia), Mariano Javier de Leon Dominguez Romero (Universidad Nacional de Córdoba), Mariel Pettee (Lawrence Berkeley National Lab), Marimuthu Kalimuthu (Universität Stuttgart), Marina Meila (University of Washington, Seattle), Mario Krenn (Max Planck Institute for the Science of Light), Marios Mattheakis (Harvard University), Matija Medvidović (ETHZ - ETH Zurich), Matiwos Mebratu (Stanford University), Matt L. Sampson (Princeton University), Matteo Manica (International Business Machines), Maximilian Dax (Max-Planck Institute), Maxwell Xu Cai (SURF Corporative), Micah Bowles (University of Oxford), Michael Deistler (University of Tuebingen), Mike Williams (Massachusetts Institute of Technology), Milind Malshe (Georgia Institute of Technology), Mira Moukheiber (Massachusetts Institute of Technology), Mohammad Shahab Sepehri (University of Southern California), Mohammadamin Tavakoli (California Institute of Technology), Mohannad Elhamod (Virginia Polytechnic Institute and State University), Mridul Khurana (Virginia Polytechnic Institute and State University), Nadim Saad (Northeastern University), Natalie Klein (Los Alamos National Laboratory), Nayantara Mudur (Harvard University), Neel Chatterjee (Intel), Neerav Kaushal (Sail Biomedicines), Negin Forouzesh (California State University, Los Angeles), Nesar Soorve Ramachandra (Argonne National Laboratory), Nick McGreivy (Princeton University), Nicolò Oreste Pinciroli Vago (Polytechnic Institute of Milan), Nils Thuerey (Technical University Munich), Noemi Anau Montel (University of Amsterdam), Olivier Saut (CNRS), Ori Linial (Technion - Israel Institute of Technology, Technion), Othmane Rifki (DESY), Pao-Hsiung Chiu (Institute of High Performance Computing, Singapore, A*STAR), Pedro L. C. Rodrigues (Inria), Peer-timo Bremer (University of Utah), Peimeng Guan (Georgia Institute of Technology), Peter McKeown (CERN), Peter Melchior (Princeton University), Peter Nugent (University of Oklahoma), Peter Steinbach (Helmholtz-Zentrum Dresden-Rossendorf), Phaedon Stelios Koutsourelakis (Technische Universität München), Phan Nguyen (Lawrence Livermore National Labs), Pierre Thodoroff (University of Cambridge), Pietro Vischia (Universidad de Oviedo), Pim De Haan (University of Amsterdam), Pradyun Hebbar (Lawrence Berkeley National Lab), Progyan Das (Indian Institute of Technology, Gandhinagar), Qi Tang (Georgia Institute of Technology), Qiaohao Liang (Massachusetts Institute of Technology), Rafael Gomez-Bombarelli (Massachusetts Institute of Technology), Raghav Kansal (CERN), Raheem Karim Hashmani (University of Wisconsin - Madison), Rahul Ghosh (University of Minnesota, Minneapolis), Rama Vasudevan (Oak Ridge National Laboratory), Rasmus F. Ørsøe (Technische Universität München), Redouane Lguensat (Institut Pierre-Simon Laplace), Remmy Zen (Max Planck Institute for the Science of Light), Reza Akbarian Bafghi (University of Colorado at Boulder), Rhys Goodall (Chemix Inc.), Ricardo Vinuesa (KTH Royal Institute of Technology), Richard M. Feder (University of California, Berkeley), Rikab Gambhir (Massachusetts Institute of Technology), Roberto Bondesan (Imperial College London), Rodrigo Vargas-Hernandez (McMaster University), Rohan Venkat (University of Chicago), Ronan Legin (Université de Montréal), Rutuja Gurav (University of California, Riverside), Ryan Hausen (Johns Hopkins University), Sam Foreman (Argonne National Laboratory), Samson J Koelle (Amazon), Sandeep Madireddy (Argonne National Laboratory), Sankalp Gilda (DevelopYours), Sarvesh Gharat (Indian Institute of Technology, Bombay), Sarvesh Kumar Yadav (Raman Research Institute), Sascha Caron (Radboud University Nijmegen), Savannah Jennifer Thais (Columbia University), Sebastian Dorn (Technical University of Applied Sciences Augsburg), Sebastian Kaltenbach (ETHZ - ETH Zurich), Shaokai Yang (University of Alberta), Shaoming Xu (University of Minnesota - Twin Cities), Shashank Galla (Texas A&M University - College Station), Shixiao Liang (Rice University), Shiyu Wang (Emory University), Shriram Chennakesavalu (Stanford University), Shubhendu Trivedi (Massachusetts Institute of Technology), Siddhant Midha (Princeton University), Siddharth Mishra-Sharma (MIT), Sifan Wang (Yale University), Sining Huang (University of California, Berkeley), Sirisha Rambhatla (University of Waterloo), Somya Sharma (University of Minnesota - Twin Cities), Soronzonbold OTGONBAATAR (Ludwig-Maximilians-Universität München), Sreevani Jarugula (Fermilab), Srinadh Reddy Bhavanam (Clemson University), Srinandan Dasmahapatra (University of Southampton), Stefan M. Wild (Lawrence Berkeley National Lab), Stephan Günnemann (Technical University Munich), Stephen Zhang (University of Melbourne), Sudhakar Pamidighantam (Georgia Institute of Technology), Sui Tang (UC Santa Barbara), Sunbochen Tang (Massachusetts Institute of Technology), Supranta Sarma Boruah (University of Pennsylvania, University of Pennsylvania), Taniya Kapoor (Delft University of Technology), Taoli Cheng (University of Montreal), Tarun Kumar (Hewlett Packard Enterprise), Tatiana Likhomanenko (Apple), Thomas Beckers (Vanderbilt University), Tianji Cai (SLAC National Accelerator Laboratory), Tiffany Fan (Stanford University), Till Korten (Helmholtz Zentrum Dresden Rossendorf (HZDR)), Tobias Buck (Heidelberg University, Ruprecht-Karls-Universität Heidelberg), Tomo Lazovich (U.S. Census Bureau), Tri Nguyen (Massachusetts Institute of Technology), Tristan Cazenave (Université Paris-Dauphine (Paris IX)), Udit Bhatia (IIT Gandhinagar, Dhirubhai Ambani Institute Of Information and Communication Technology), V Ashley Villar (Harvard University), Vahe Gharakhanyan (Facebook), Vignesh C Bhethanabotla (California Institute of Technology), Vinicius Mikuni (Lawrence Berkeley National Lab), Vishal Dey (Ohio State University, Columbus), Vishwa Pardeshi (Fidelity Investments), Vitus Benson (Max-Planck-Institute for Biogeochemistry), Volkan Kumtepeli (University of Oxford), Vudtiwat Ngampruetikorn (University of Sydney), Wai Tong Chung (Together AI), Wenhao Lu (Universität Hamburg), Wonmin Byeon (NVIDIA), Xian Yeow Lee (Hitachi America Ltd.), Xiang Li (University of Minnesota - Twin Cities), Xiao-Yong Jin (Argonne National Laboratory), Xiaowei Jia (University of Pittsburgh), Xihaier Luo (Brookhaven National Laboratory), Xinyan Li (IQVIA), Yangzesheng Sun (Meshy AI), Yannik Glaser (University of Hawaii at Manoa), Yao Fehlis (Advanced Micro Devices), Yilin Chen (Stanford University), Yin Li (Peng Cheng Laboratory), Yingtao Luo (CMU, Carnegie Mellon University), Yiran Wang (Xidian University), Yitian Sun (McGill University), Yixiao Kang (Facebook), Yiyi Tao (ByteDance Inc.), Youngwoo Cho (Korea Advanced Institute of Science and Technology), Yuan Yin (Valeo), Yuanqing Wang (New York University), Yukun Song (University of California, Berkeley), Yunxuan Li (Google), Zefang Liu (Georgia Institute of Technology), Zhe Jiang (University of Florida), Zhida Huang (ByteDance Inc.), Zhuo Chen (Massachusetts Institute of Technology), Ziming Liu (Massachusetts Institute of Technology), Zixing Song (The Chinese University of Hong Kong), Zixuan Wang (CMU, Carnegie Mellon University)

Call for external contributions

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, earth science, biophysics, and related sciences; and
using physical insights to understand and/or improve machine learning techniques, for instance building hybrid machine learning algorithms that leverage physical models with machine learning blocks to create interpretable and accurate predictive models.

To this end, we encourage external contributions, which will be presented during in-person poster sessions during the workshop. Selected contributions will be offered 15-minute contributed talks. 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 into 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.
Other areas: 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, scientific foundation models, probabilistic programming, simulation-based inference, variational inference, causal inference, etc.

Submission tracks

Research abstract: We invite contributions on either completed or high-quality work-in-progress original research on the topics outlined above.
Datasets & Benchmarks abstract: We invite contributions describing a dataset and/or corresponding benchmarks at the intersection of ML and Physical Sciences, in particular showcasing the unique nature of physical datasets and forward models in the context of ML applications. See submission and review guidelines for specific instructions relating to this track.
Perspectives abstract: This year, we introduce a new Perspectives track, where researchers have the opportunity to present compelling and grounded viewpoints on recent directions and open questions at the intersection of ML and Physical Sciences. This track encourages disseminating perspectives on past, present, or future challenges of interest to scientists working at the intersection between ML and Physical Sciences. The track aims to stimulate productive and respectful conversations on timely topics that will benefit from the ML4PS workshop's attendees' input. Position papers should meet standard scientific rigor, including using evidence and reasoning to support claims, including relevant background and context, and attributing others' work via appropriate citations. Accepted Perspectives will be presented at the workshop during the poster sessions.

Submission instructions

Submissions should be anonymized short papers (extended abstracts) up to 4 pages excluding references. We invite authors to follow the guidelines and best practices from the NeurIPS conference (see also the main conference Datasets & Benchmarks call for guidelines pertaining to the corresponding track). Please ensure that your paper is approachable by someone 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 (optionally, single-blind for the Datasets & Benchmarks track). All authors must be registered as authors 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.

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

Submit contributions Submission guidelines

Code sharing & reproducibility guidance: While we do not enforce it, we will highlight submissions containing documented code and reproducible workflows through a Reproducibility Badge.

Double-submission policy: While we primarily encourage the submission of original pieces of work, we also accept submissions that are extended abstract versions of already published work if their topic fits particularly well with the workshop's scope. In contrast, with the objective of respecting the hard work of reviewers and giving equal chances to all submissions, we strictly prohibit submitting to multiple workshops simultaneously. Submissions flagged as coincidentally submitted to multiple NeurIPS workshops will be desk rejected.