Machine learning methods have had great success in learning complex representations of data that enable novel modeling and data processing approaches in many scientific disciplines. Physical sciences span problems and challenges at all scales in the universe: from finding exoplanets in trillions of sky pixels, to finding machine learning inspired solutions to the quantum many-body problem, to detecting anomalies in event streams from the Large Hadron Collider, to predicting how extreme weather events will vary with climate change. Tackling a number of associated data-intensive tasks including, but not limited to, segmentation, 3D computer vision, sequence modeling, causal reasoning, generative modeling, and efficient probabilistic inference are critical for furthering scientific discovery. In addition to using machine learning models for scientific discovery, the ability to interpret what a model has learned is receiving an increasing amount of attention.

In this targeted workshop, we aim to bring together computer scientists, mathematicians and physical scientists who are interested in applying machine learning to various outstanding physical problems, including in inverse problems; approximating physical processes; understanding what a learned model really represents; and connecting tools and insights from the physical sciences to the study of machine learning models. In particular, the workshop invites researchers to contribute short papers that demonstrate cutting-edge progress in the application of machine learning techniques to real-world problems in the physical sciences, and using physical insights to understand what the learned model represents.

By bringing together machine learning researchers and physical scientists who apply machine learning, we expect to strengthen the 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.

NeurIPS 2019

The Machine Learning and the Physical Sciences 2019 workshop will be held on December 14, 2019 as a part of the 33rd Annual Conference on Neural Information Processing Systems, at the Vancouver Convention Center, Vancouver, Canada. Please check the main conference website and FAQ for information about registration, schedule, venue, and other arrangements. More information in the Registration section below.


Invited Speakers

Contributed Speakers


08:10 – 08:20 Opening remarks

08:20 – 09:00 Invited talk 1
Bernhard Schölkopf (Max Planck Institute for Intelligent Systems)
09:00 – 09:20 Contributed talk 1
Rose Yu (Northeastern University)
Towards physics-informed deep learning for turbulent flow prediction
09:20 – 09:40 Contributed talk 2
Samuel Schoenholz (Google Brain)
JAX, M.D.: End-to-End Differentiable, Hardware Accelerated, Molecular Dynamics in Pure Python
09:40 – 10:40 Morning poster session and coffee break

10:40 – 11:20 Invited talk 2
Katie Bouman (California Institute of Technology)
11:20 – 12:00 Invited talk 3
Alán Aspuru-Guzik (University of Toronto)
Recent progress in ML for chemistry: SELFIES, inverse design of drug candidates and materials, and Bayesian algorithms for self-driving laboratories.
12:00 – 12:20 Contributed talk 3
Alvaro Sanchez-Gonzalez (DeepMind)
Hamiltonian Graph Networks with ODE Integrators
12:20 – 14:00 Lunch break

14:00 – 14:40 Invited talk 4
Maria Schuld (Xanadu)
14:40 – 15:20 Invited talk 5
Lenka Zdeborova (Institut de Physique Théorique)
Understanding machine learning via exactly solvable statistical physics models
15:20 – 16:20 Afternoon poster session and coffee break

16:20 – 17:00 Invited talk 6
Yasaman Bahri (Google Brain)
17:00 – 17:20 Contributed talk 4
Danilo Jimenez Rezende (DeepMind)
Equivariant Hamiltonian Flows
17:20 – 17:40 Contributed talk 5
Eric Metodiev (MIT)
Metric Methods with Open Collider Data
17:40 – 18:00 Contributed talk 6
Miles Cranmer (Princeton University)
Learning Symbolic Physics with Graph Networks


Steering Committee

Call for papers

We invite researchers to submit work particularly in the following and related areas:

  • Application of machine and deep learning to physical sciences
  • Generative models
  • Likelihood-free inference
  • Variational inference
  • Simulation-based models
  • Implicit models
  • Probabilistic models
  • Model interpretability
  • Approximate Bayesian computation
  • Strategies for incorporating prior scientific knowledge into machine learning algorithms
  • Experimental design
  • Any other area related to the subject of the workshop

Submissions of completed projects as well as high-quality works in progress are welcome. All accepted short papers 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 short papers that overlap with papers that are under review or have been recently published in a conference or a journal. However, we do not accept cross submissions of the same short paper to multiple workshops at NeurIPS. Submissions will be kept confidential until they are accepted and authors confirm that they can be included in the workshop. If a submission is not accepted, or withdrawn for any reason, it will be kept confidential and not made public.

Accepted submissions will be presented as posters during the workshop. Several accepted submissions will be selected for contributed talks.

Submission instructions

Submissions should be anonymized short papers up to 4 pages in PDF format, typeset using the NeurIPS style. References do not count towards the page limit. Appendices are discouraged, and reviewers are not expected to read beyond the first 4 pages. A workshop-specific modified NeurIPS style file will be provided for the camera-ready versions, after the author notification date.

Please note that at least one coauthor of each accepted paper will be expected to attend the workshop in person to present a poster or give a contributed talk. Submissions page is here.

Submit paper

Instructions for Accepted Papers, Posters, and Digital Acceptances

Accepted papers (either as talks, posters, or digital acceptances)

Please produce a "camera-ready" (final) version of your accepted paper by replacing the "neurips_2019.sty" style file with the "neurips_2019_ml4ps.sty" file available here and using the "final" package option (that is, "\usepackage[final]{neurips_2019_ml4ps}") to include author and affiliation information. The modified style file replaces the first page footer to refer to the workshop instead of the main conference. It is acceptable if your paper goes up to five pages (excluding the references or any appendices) due to author and affiliation information taking extra space on the first page.

Please revise your paper as much as you can to reasonably address reviewer commens. The revision would include minor corrections and/or changes to directly address reviewer comments. Beyond these points, it is not acceptable to include any significant new material that was not present in the reviewed version of your paper.

Please upload the final PDF of your paper as an updated version in your existing submission on EasyChair by the camera-ready deadline.

Posters at the Workshop

If your paper was accepted as a poster presentation, we expect that you produce a poster and present it at one of the two poster sessions (morning and afternoon) at the workshop. The workshop schedule will show which session your poster is assigned to. NeurIPS suggests that you use A0 size in portrait mode and no larger than 36W x 48H inches or 90 x 122 cm. Posters are taped to the wall. Posters should be on lightweight paper, not laminated. NeurIPS will provide the tape. All posters will be presented inside the room the workshop takes place in and there are no poster boards at workshops.

Accepted Talks

If your paper was accepted as a contributed talk, presenting a poster at the workshop is optional, meaning that you can prepare and present a poster at the workshop if you choose to do so.

Digital Acceptances

If your paper was accepted as a "digital acceptance", this means that you won't have a possibility to present the work at the workshop in person. However, your paper will be listed in the workshop website along with the other accepted papers. Please produce your camera-ready paper as instructed above. Optionally you can send us, via this online form a single slide in PDF format that will serve as a spotlight of your work that we will show as a cycling presentation during the workshop poster sessions. These single slides should include the names of co-authors and paper title at the bottom of the slide and be preferably in 16:9 aspect ratio.

Travel support

Thanks to our sponsors Vector Institute, Flatiron Institute, IRIS-HEP, and DeepMind, we will be able to offer travel support for a number of presenters with posters and/or talks at the workshop. Presenters indicate their interest using a web form shared with co-authors of accepted papers. This excludes papers in the digital acceptance category because these are not going to be presented in person. Funding will be distributed based on need with preference given to junior researchers and those from under-represented backgrounds.

Important dates

  • Submission deadline: September 9, 2019 September 16, 2019, 23:59 PDT
  • Author notification: October 1, 2019
  • Camera-ready (final) paper deadline: November 1, 2019, 23:59 PDT
  • Workshop: December 14, 2019


NeurIPS conference has three main sessions (Tutorials, Conference, Workshops) to which you can register. You need to be registered to at least the Workshop session in order to be able to attend this workshop. For pricing information see NeurIPS 2019 Pricing. Registrations this year are handled using a randomized lottery, due to the demand for registrations surpassing the available capacity (this led to registrations selling out under 12 minutes for NeurIPS 2018). The official information regarding this can be accessed here.

Between September 6 and September 20, 2019, those wishing to attend NeurIPS will indicate their intention to register (intention to purchase a ticket) by signing up for the lottery at the main conference home page. On September 20, 2019, registration invitations will be sent to people randomly picked from the lottery pool, which will allow them to register (purchase a ticket) within two weeks. The invitation will expire after two weeks.

In addition to—and independent from—the regular lottery registration, this workshop will have access to a number of registration slots reserved for workshop presenters (oral or poster), which we will assign to the main presenters of accepted work after the author notification date of October 1, 2019. We will do our best to ensure that the main presenters of all accepted work will get a registration invitation through this channel (they will still need to purchase a ticket), if they were not able to register through the lottery. However, this is not guaranteed and the number of reserved registrations this workshop will get access to is beyond our control. THEREFORE WE STRONGLY ENCOURAGE THE MAIN PRESENTERS OF ALL SUBMITTED WORK TO ENTER THE REGISTRATION LOTTERY IN ORDER TO ENSURE THAT THEY GET A REGISTRATION INVITATION THROUGH AT LEAST ONE CHANNEL.


We have accepted 91 short papers for poster presentation at the workshop. Six of these are selected for contributed talks.

Due to the large volume of good-quality papers submitted to this workshop, we introduced a "digital acceptance" category to designate accepted papers without in person (poster or talk) representation, inspired by the ML4H workshop. These have review scores above the rejection threshold, but are beyond our venue capacity limitations when all papers are sorted by review score. We have accepted 70 short papers as digital acceptances.

Poster session (Morning)

1 3D Conditional Generative Adversarial Networks to enable large-scale seismic image enhancement [pdf]
Praneet Dutta, Bruce Power, Adam Halpert, Carlos Ezequiel, Aravind Subramanian, Massimo Mascaro, Kenton Prindle, Chanchal Chaterjee, Vishal Vaddina, Sindhu Hari, Andrew Leach, Raj Domala and Laura Bandura
2 DeepXDE: A deep learning library for solving differential equations [pdf]
Lu Lu, Xuhui Meng, Zhiping Mao and George Karniadakis
3 Metric Methods with Open Collider Data [pdf]
Eric Metodiev, Patrick Komiske, Radha Mastandrea, Preskha Naik and Jesse Thaler
4 Deep Learning Model for Finding New Superconductors [pdf]
Tomohiko Konno, Hodaka Kurokawa, Fuyuki Nabeshima, Yuki Sakishita, Ryo Ogawa, Iwao Hosako and Atsutaka Meda
5 deepCR: Cosmic Ray Rejection with Deep Learning [pdf]
Keming Zhang and Joshua S. Bloom
6 LSTM-Designed Quantum Experiments [pdf]
Thomas Adler, Manuel Erhard, Mario Krenn, Johannes Brandstetter, Johannes Kofler and Sepp Hochreiter
7 Quantum Natural Gradient [pdf]
James Stokes, Josh Izaac, Nathan Killoran and Giuseppe Carleo
8 GMLS-Nets: Scientific Machine Learning Methods for Unstructured Data [pdf]
Nathaniel Trask, Ravi Patel, Ben Gross and Paul Atzberger
9 Likelihood-free inference with an improved cross-entropy estimator [pdf]
Markus Stoye, Johann Brehmer, Gilles Louppe, Juan Pavez and Kyle Cranmer
10 Efficient training of energy-based models via spin-glass control [pdf]
Alejandro Pozas-Kerstjens, Gorka Muñoz-Gil, Miguel Angel Garcia-March, Antonio Acin, Maciej Lewenstein and Przemyslaw R. Grzybowski
11 Deep convolutional neural networks for multi-scale time-series classification and application to disruption prediction in fusion devices [pdf]
R.M. Churchill and Diii-D Team
12 Multi-fidelity Learning with Heterogeneous Domains [pdf]
Soumalya Sarkar, Michael Joly and Paris Perdikaris
13 A million times speed up in parameters retrieval with deep learning [pdf]
Muhammad Firmansyah Kasim, Jacob Topp-Mugglestone, Peter Hatfield, Dustin Froula, Gianluca Gregori, Matt Jarvis, Eleonora Viezzer and Sam Vinko
14 All SMILES Variational Autoencoder [pdf]
Zaccary Alperstein, Artem Cherkasov and Jason Rolfe
15 Learning Symbolic Physics with Graph Networks [pdf]
Miles Cranmer, Rui Xu, Peter Battaglia and Shirley Ho
16 Mining gold: Improving simulation-based inference with latent information [pdf]
Johann Brehmer, Kyle Cranmer, Siddharth Mishra-Sharma, Felix Kling and Gilles Louppe
17 Equivariant Hamiltonian Flows [pdf]
Danilo Jimenez Rezende, Irina Higgins, Sébastien Racaniere and Peter Toth
18 Producing High-fidelity Flux Fields From N-body Simulations Using Physically Motivated Neural Networks [pdf]
Peter Harrington, Mustafa Mustafa, Max Dornfest, Wahid Bhimji and Zarija Lukic
19 ChemBO: Bayesian Optimization of Small Organic Molecules with Synthesizable Recommendations [pdf]
Ksenia Korovina, Sailun Xu, Kirthevasan Kandasamy, Willie Neiswanger, Barnabas Poczos, Jeff Schneider and Eric P. Xing
20 Sim-to-Real Domain Adaptation For High Energy Physics [pdf]
Marouen Baalouch, Maxime Defurne, Jean-Philippe Poli and Noëlie Cherrier
21 Augmenting Genetic Algorithms with Neural Networks [pdf]
Akshatkumar Nigam, Pascal Friederich, Mario Krenn and Alán Aspuru-Guzik
22 Learning to Control PDEs with Differentiable Physics [pdf]
Nils Thuerey, Philipp Holl and Vladlen Koltun
23 Molecule-Augmented Attention Transformer [pdf]
Lukasz Maziarka, Tomasz Danel, Slawomir Mucha, Krzysztof Rataj, Jacek Tabor and Stanislaw Jastrzebski
24 Physics-guided Reinforcement Learning for 3D Molecular Structures [pdf]
Sookyung Kim, Youngwoo Cho, Peggy. Pk Li, Mike. P Surh and T. Yong-Jin Han
25 The Learnability Scaling of Quantum States: Restricted Boltzmann Machines [pdf]
Anna Golubeva, Roger Melko, Dan Sehayek, Giacomo Torlai, Bohdan Kulchytskyy and Michael Albergo
26 Sign Structure of Many-Body Wavefunctions and Machine Learning [pdf]
Tom Westerhout, Nikita Astrakhantsev, Konstantin S. Tikhonov, Mikhail I. Katsnelson and Andrey A. Bagrov
27 Offline Contextual Bayesian Optimization for Nuclear Fusion [pdf]
Youngseog Chung, Ian Char, Willie Neiswanger, Kirthevasan Kandasamy, Andrew Nelson, Mark Boyer, Egemen Kolemen and Jeff Schneider
28 Physics-informed Autoencoders for Lyapunov-stable Fluid Flow Prediction [pdf]
N. Benjamin Erichson, Michael Muehlebach and Michael Mahoney
29 A neural network oracle for quantum nonlocality problems in networks [pdf]
Tamás Kriváchy, Yu Cai, Daniel Cavalcanti, Arash Tavakoli, Nicolas Gisin and Nicolas Brunner
30 Hamiltonian Graph Networks with ODE Integrators [pdf]
Alvaro Sanchez-Gonzalez, Victor Bapst, Kyle Cranmer and Peter Battaglia
31 Hashing and metric learning for charged particle tracking [pdf]
Sabrina Amrouche, Tobias Golling, Moritz Kiehn and Andreas Salzburger
32 Scalable Extreme Deconvolution [pdf]
James A. Ritchie and Iain Murray
33 Inference of a Universal Ornstein-Zernike Closure Relationship with Machine Learning [pdf]
Rhys Goodall and Alpha Lee
34 Learning quantum states from noisy data [pdf]
Brian Timar, Giacomo Torlai, Evert van Nieuwenburg, Manuel Endres and Roger Melko
35 Reinforcement-Learning-Based Variational Quantum Circuits Optimization for Combinatorial Problems [pdf]
Sami Khairy, Ruslan Shaydulin, Lukasz Cincio, Yuri Alexeev and Prasanna Balaprakash
36 Trigger Rate Anomaly Detection with Conditional Variational Autoencoders at the CMS Experiment [pdf]
Adrian Pol, Victor Berger, Gianluca Cerminara, Cecile Germain and Maurizio Pierini
37 Exploring Generative Physics Models with Scientific Priors in Inertial Confinement Fusion [pdf]
Rushil Anirudh, Jayaraman J. Thiagarajan, Shusen Liu, Peer-Timo Bremer and Brian K. Spears
38 PaccMann RL : Designing anticancer drugs from transcriptomic data via reinforcement learning [pdf]
Jannis Born, Matteo Manica, Ali Oskooei, Joris Cadow and María Rodríguez Martínez
39 The Chemistry of Smell: Learning Generalizable Perceptual Representations of Small Molecules [pdf]
Benjamin Sanchez-Lengeling, Jennifer Wei, Brian K Lee, Richard C Gerkin, Alán Aspuru-Guzik and Alexander B Wiltschko
40 Data Driven Simulation of Cherenkov Detectors using Generative Adversarial Network [pdf]
Denis Derkach, Artem Maevskiy, Nikita Kazeev, Andrey Usyuzhanin, Maksim Artemev and Lucio Anderlini
41 Applying Bayesian Optimization to Understand Tradeoffs for Antireflective Optical Designs [pdf]
Michael Mccourt, Bolong Cheng, Sajad Haghanifar and Paul Leu
42 HIGAN: Cosmic Neutral Hydrogen with GANs [pdf]
Atakan Okan, Juan Zamudio-Fernandez, Francisco Villaescusa, Seda Bilaloglu, Siyu He, Laurence Levasseur, Asena Derin Cengiz and Shirley Ho
43 Value-Added Chemical Discovery Using Reinforcement Learning [pdf]
Peihong Jiang, Hieu Doan, Sandeep Madireddy, Rajeev Surendran Assary and Prasanna Balaprakash
44 Predicting ground state configuration of energy landscape using graph neural network [pdf]
Seong Ho Pahng and Michael Brenner
45 Manifold coordinates with physical meaning [pdf]
Samson Koelle, Hanyu Zhang, Marina Meila and Yu-Chia Chen

Poster session (Afternoon)

46 Model Bridging: To Interpretable Simulation Model From Neural Network [pdf]
Keiichi Kisamori, Keisuke Yamazaki, Yuto Komori and Hiroshi Tokieda
47 Predicting hydrogen storage in nanoporous materials using meta-learning [pdf]
Yangzesheng Sun, Robert F. DeJaco and J. Ilja Siepmann
48 Unsupervised Star Galaxy Classification with Cascade Variational Auto-Encoder [pdf]
Hao Sun, Jiadong Guo, Edward J. Kim and Robert J. Brunner
49 A Taylor Based Sampling Scheme for Machine Learning in Computational Physics [pdf]
Paul Novello, Gael Poette, David Lugato and Pietro Congedo
50 Guided Selection of Accurate Belief Propagation Fixed Points [pdf]
Christian Knoll and Franz Pernkopf
51 Data-driven Chemical Reaction Classification [pdf]
Philippe Schwaller, Alain Claude Vaucher, Vishnu H Nair and Teodoro Laino
52 Bayesian Inversion Of Generative Models For Geologic Storage Of Carbon Dioxide [pdf]
Gavin Graham and Yan Chen
53 Jet classification techniques in CMS [pdf]
Mauro Verzetti
54 Fast Wiener filtering of CMB maps with Neural Networks [pdf]
Moritz Munchmeyer and Kendrick Smith
55 Designing Deep Inverse Models for History Matching in Reservoir Simulations [pdf]
Vivek Sivaraman Narayanaswamy, Jayaraman J Thiagarajan, Rushil Anirudh, Fahim Forouzanfar, Peer-Timo Bremer and Xiao-Hui Wu
56 Single-Frame Super-Resolution of Solar Magnetograms: Investigating Physics-Based Metrics & Losses [pdf]
Anna Jungbluth, Xavier Gitiaux, Shane Maloney, Carl Shneider, Paul Wright, Atılım Güneş Baydin, Michel Deudon, Alfredo Kalaitzis, Yarin Gal and Andres Munoz-Jaramillo
57 Training Deep Neural Networks by optimizing over nonlocal paths in hyperparameter space [pdf]
Vlad Pushkarov, Jonathan Efroni, Mykola Maksymenko and Maciej Koch-Janusz
58 Using U-Nets to create high-fidelity virtual observations of the solar corona [pdf]
Valentina Salvatelli, Souvik Bose, Brad Neuberg, Luiz F. G. dos Santos, Mark Cheung, Miho Janvier, Atılım Güneş Baydin, Yarin Gal and Meng Jin
59 From Dark Matter to Galaxies with Convolutional Neural Networks [pdf]
Jacky H. T. Yip, Xinyue Zhang, Yanfang Wang, Wei Zhang, Yueqiu Sun, Gabriella Contardo, Francisco Villaescusa-Navarro, Siyu He, Shy Genel and Shirley Ho
60 Automatic Determination of Chemical Reaction Mechanisms with Neural Networks [pdf]
Daniel Levine
61 Learning Dynamical Systems from Partial Observations [pdf]
Ibrahim Ayed, Emmanuel de Bézenac, Arthur Pajot, Patrick Gallinari and Julien Brajard
62 Data-driven discovery of free-form governing differential equations [pdf]
Steven Atkinson, Waad Subber, Liping Wang, Genghis Khan, Philippe Hawi and Roger Ghanem
63 Deep neural network solution of the electronic Schrödinger equation [pdf]
Jan Herrmann, Zeno Schätzle and Frank Noe
64 Accelerated Machine Learning as a Service for Particle Physics Computing [pdf]
Nhan Tran, Javier Duarte, Philip Harris, Scott Hauck, Burt Holzman, Shih-Chieh Hsu, Sergo Jindariani, Suffian Khan, Benjamin Kreis, Mia Liu, Ted Way, Vladimir Loncar, Jennifer Ngadiuba, Kevin Pedro, Maurizio Pierini, Dylan Rankin, Matthew Trahms, Aristeidis Tsaris, Colin Versteeg, Dustin Werran, Zhenbin Wu and Thomas Klijnsma
65 Predicting Weather Uncertainty with Deep Convnets [pdf]
Peter Grönquist, Tal Ben-Nun, Nikoli Dryden, Peter Dueben, Luca Lavarini, Shigang Li and Torsten Hoefler
66 Using Single Protein/Ligand Binding Models to Predict Active Ligands for Previously Unseen Proteins [pdf]
Vikram Sundar and Lucy Colwell
67 Turbulence forecasting via Neural ODE [pdf]
Gavin Portwood, Peetak Mitra, Mateus Dias Ribeiro, Tan Minh Nguyen, Balasubramanya Nadiga, Juan Saenz, Micheal Chertkov, Animesh Garg, Anima Anandkumar, Andreas Dengel, Richard Baraniuk and David Schmidt
68 Application of distance-weighted graph networks to real-life particle detector output [pdf]
Gianluca Cerminara, Abhijay Gupta, Yutaro Iiyama, Jan Kieseler, Maurizio Pierini, Marcel Rieger, Gerrit Van Onsem and Kinga Wozniak
69 Predicting Features of Quantum Systems using Classical Shadows [pdf]
Hsin-Yuan Huang and Richard Kueng
70 Hybrid Physical-Deep Learning Model for Astronomical Inverse Problems [pdf]
Francois Lanusse, Peter Melchior and Fred Moolekamp
71 Interaction networks for the identification of Higgs boson decays to bottom quark-antiquark pairs [pdf]
Javier Duarte, Olmo Cerri, Eric Moreno, Thong Nguyen, Harvey Newman, Jean-Roch Vlimant, Avikar Periwal, Aidana Serikova, Maria Spiropulu and Maurizio Pierini
72 Reservoir Computing for Prediction of Beam Evolution in Particle Accelerators [pdf]
Heidi Komkov, Levon Dovlatyan, Artur Perevalov and Daniel Lathrop
73 Adaptive Quantum State Tomography with Neural Networks [pdf]
Yihui Quek, Stanislav Fort and Hui Khoon Ng
74 Low-latency machine learning inference on FPGAs [pdf]
Javier Duarte, Christian Herwig, Burt Holzman, Sergo Jindariani, Benjamin Kreis, Mia Liu, Ryan Rivera, Nhan Tran, Vladimir Loncar, Jennifer Ngadiuba, Maurizio Pierini, Sioni Summers, Scott Hauck, Shih-Chieh Hsu, Zhenbin Wu, Edward Kreinar, Song Han, Phil Harris and Dylan Rankin
75 Numerical Weather Model Super-Resolution [pdf]
Alok Singh, Brian White and Adrian Albert
76 Correlation of Auroral Dynamics and GNSS Scintillation with an Autoencoder [pdf]
Kara Lamb, Garima Malhotra, Athanasios Vlontzos, Edward Wagstaff, Atılım Güneş Baydin, Anahita Bhiwandiwalla, Yarin Gal, Alfredo Kalaitzis, Anthony Reina and Asti Bhatt
77 Convolutional neural networks for energy and vertex reconstruction in DUNE [pdf]
Ilsoo Seong, Lars Hertel, Julian Collado, Pierre Baldi, Jianming Bian, Lingge Li and Nitish Nayak
78 Calculating Renyi Entropies with Neural Autoregressive Quantum States [pdf]
Zhaoyou Wang and Emily Davis
79 Equivariant Flows: sampling configurations for multi-body systems with symmetric energiess [pdf]
Jonas Köhler, Leon Klein and Frank Noe
80 Refined Redshift Regression in Cosmology with Graph Convolution Networks [pdf]
Robert Beck, Yannik Glaser, Peter Sadowski and Istvan Szapudi
81 Towards physics-informed deep learning for turbulent flow prediction [pdf]
Rui Wang, Karthik Kashinath, Mustafa Mustafa and Rose Yu
82 Learning Evolution of Coupled Dynamical Systems with Integrated Data-driven and Model-based Approach [pdf]
Priyabrata Saha, Arslan Ali, Burhan Mudassar, Yun Long and Saibal Mukhopadhyay
83 Graph Neural Networks for Particle Reconstruction in High Energy Physics detectors [pdf]
Xiangyang Ju, Steven Farrell, Paolo Calafiura, Lindsey Gray, Jean-Roch Vlimant, Prabhat, Daniel Murnane, Thomas Klijnsma, Kevin Pedro, Giuseppe Cerati, Jim Kowalkowski, Gabriel Perdue, Panagiotis Spentzouris, Nhan Tran, Alexander Zlokapa, Joosep Pata, Maria Spiropulu, Sitong An, Adam Aurisano, Jeremy Hewes, Aristeidis Tsaris, Kazuhiro Terao and Tracy Usher
84 Machine learning climate variability [pdf]
Ji Hwan Park, Shinjae Yoo and Balasubramanya Nadiga
85 Online tuning and light source control using physics-informed Gaussian process [pdf]
Adi Hanuka, Joe Duris, Xiaobiao Huang, Jane Shtalenkova and Dylan Kennedy
86 JAX, M.D.: End-to-End Differentiable, Hardware Accelerated, Molecular Dynamics in Pure Python [pdf]
Samuel Schoenholz and Ekin Cubuk
87 Interpretability Study on Deep Learning for Jet Physics at the Large Hadron Collider [pdf]
Taoli Cheng
88 Heteroscedastic Bayesian Optimisation in Scientific Discovery [pdf]
Ryan-Rhys Griffiths, Miguel Garcia-Ortegon, Alexander Aldrick and Alpha Lee
89 AI Safety for High Energy Physics [pdf]
Chase Shimmin and Benjamin Nachman
90 Machine Learning Models for Optimization and Control of X-ray Free Electron Lasers [pdf]
Auralee Edelen, Nicole Neveu, Daniel Ratner, Claudio Emma and Christopher Mayes
91 Deep learning for Aerosol Forecasting [pdf]
Caleb Hoyne, Surya Karthik Mukkavilli and David Meger

Digital acceptances

92 Hierarchical variational models for statistical physics [pdf]
Jaan Altosaar, Rajesh Ranganath and Kyle Cranmer
93 A Two-Step Graph Convolutional Decoder for Molecule Generation [pdf]
Xavier Bresson and Thomas Laurent
94 Microlensing Light-curve Anomaly Detection [pdf]
Antonio Herrera Martin and Michael Albrow
95 Multi-Scale Graph Partitioning for Unravelling Dynamics of Major Peanut Allergen Ara h 1 [pdf]
Heng Zhang
96 Single trajectory characterization via machine learning [pdf]
Gorka Muñoz-Gil, Miguel Ángel García-March, Carlo Manzo, José David Martín-Guerrero and Maciej Lewenstein
97 Emulation of cosmological mass maps with conditional generative adversarial networks [pdf]
Nathanael Perraudin, Sandro Marcon, Aurelien Lucchi and Tomasz Kacprzak
98 Intelligence Learning: Efficient Parameter Sampling for Emulator Construction [pdf]
Drimik Roy Chowdhury and Muhammad Kasim
99 Using recurrent neural networks for nonlinear component computation in advection-dominated reduced-order models [pdf]
Romit Maulik, Vishwas Rao, Sandeep Madireddy, Bethany Lusch and Prasanna Balaprakash
100 Learning to Reconstruct Crack Profiles for Eddy Current Nondestructive Testing [pdf]
Shaohua Li, Ayesha Anees, Yu Zhong, Zaifeng Yang, Yong Liu, Rick Siow Mong Goh and En-Xiao Liu
101 Embedded Constrained Feature Construction for High-Energy Physics Data Classification [pdf]
Noëlie Cherrier, Maxime Defurne, Jean-Philippe Poli and Franck Sabatié
102 Applications of Deep Learning Methodology in Real-Time Completion Event Recognition [pdf]
Yuchang Shen, Dingzhou Cao and Kate Ruddy
103 Model Parameter Optimization: ML-guided trans-resolution tuning of physical models [pdf]
Sam Partee, Michael Ringenburg, Benjamin Robbins and Andrew Shao
104 Understanding and Improving Transformer From a Multi-Particle Dynamic System Point of View [pdf]
Yiping Lu, Zhuohan Li, Di He, Zhiqing Sun, Bin Dong, Tao Qin, Liwei Wang and Tie-Yan Liu
105 Inverting Solar Spectropolarimetric Observations with Deep Learning [pdf]
Curt Dodds, Ian Cunningham, Lucas Tarr, Sarah Jaeggli, Tom Schad, Peter Sadowski and Xudong Sun
106 Learning Coarse-Grained Particle Latent Space with Auto-Encoders [pdf]
Wujie Wang and Rafael Gomez-Bombarelli
107 Machine learning for image-based wavefront sensing [pdf]
Pierre-Olivier Vanberg, Gilles Orban de Xivry, Olivier Absil and Gilles Louppe
108 Training atomic neural networks using fragment-based data generated in virtual reality [pdf]
Silvia Amabilino, Lars Bratholm, Simon Bennie and David Glowacki
109 Learning Generalized Quasi-Geostrophic Models Using Deep Neural Numerical Models [pdf]
Redouane Lguensat, Julien Le Sommer, Sammy Metref, Emmanuel Cosme and Ronan Fablet
110 Learning Generalized Quasi-Geostrophic Models Using Deep Neural Numerical ModelsOn the loss of learning capability inside an arrangement of neural networks [pdf]
Ivan Arraut and Diana Diaz
111 Unsupervised Learning of Deep Features Through Best-Fits for Observational Cosmology [pdf]
Christopher Murray, Sebastien Fabbro and Kwang Moo Yi
112 Lund jet images from generative and cycle-consistent adversarial networks [pdf]
Frederic Dreyer and Stefano Carrazza
113 Emulation of physical processes with Emukit [pdf]
Andrei Paleyes, Mark Pullin, Maren Mahsereci, Neil Lawrence and Javier González
114 Sequential design of point-wise measurements based on a deep generative model [pdf]
Hyungil Moon, Dominic Lennon, Leon Camenzind, Liuqi Yu, Dominik Zumbühl, Andrew Briggs, Michael Osborne, Edward Laird and Natalia Ares
115 Unsupervised learning for thermal anomaly detection on the lunar surface [pdf]
Ben Moseley, Valentin Bickel, Jerome Burelbach, Nicole Relatores, Daniel Angerhausen, Frank Soboczenski and Dennis Wingo
116 Evaluation Metrics for Single-Step Retrosynthetic Models [pdf]
Philippe Schwaller, Vishnu H Nair, Riccardo Petraglia and Teodoro Laino
117 Extracting more from boosted decision trees: A high energy physics case study [pdf]
Vidhi Lalchand
118 Generative Models for Solving Nonlinear Partial Differential Equations [pdf]
Ameya Joshi, Viraj Shah, Sambuddha Ghosal, Balaji Pokuri, Soumik Sarkar, Baskar Gananpathysubramanian and Chinmay Hegde
119 Neural Embedding for Physical Manipulations [pdf]
Lingzhi Zhang, Andong Cao, Rui Li and Jianbo Shi
120 Adversarial generation of mesoscale surfaces from small scale chemical motifs [pdf]
Kyle Mills, Corneel Casert and Isaac Tamblyn
121 Deep seq2seq architecture for DNA sequence decoding from noisy data [pdf]
Frederic Lechenault, Antoine Baker and Florent Krzakala
122 Training machine learning algorithms on background-contaminated data [pdf]
Nikita Kazeev and Maxim Borisyak
123 Deep Learning the Morphology of Dark Matter Substructure [pdf]
Michael Toomey, Stephon Alexander, Sergei Gleyzer, Evan McDonough and Emanuele Usai
124 Modeling sequences with quantum states: predicting generalization quality of generative modelse [pdf]
Tai-Danae Bradley, Edwin Stoudenmire and John Terilla
125 Searching for exotic long-lived particle states at the LHC using a deep neural network [pdf]
Robert Bainbridge, Oliver Buchmuller, Vilius Cepaitis, Matthias Komm and Alex Tapper
126 Identifying chemically identical stars using adverserial disentanglement [pdf]
Damien de Mijolla, Melissa Ness and Serena Viti
127 A Deep Approach for Reliable Subsurface Imaging in Software-Defined Ground Penetrating Radar [pdf]
Bisma Amjad, Tauseef Tauqeer, Fahad Shamshad, Rehan Hafiz and Mahboob Ur Rahman
128 Auto-Calibration of Remote Sensing Solar Telescopes with Deep Learning [pdf]
Brad Neuberg, Souvik Bose, Valentina Salvatelli, Luiz F. G. dos Santos, Mark Cheung, Miho Janvier, Atılım Güneş Baydin, Yarin Gal and Meng Jin
129 Hunting for Dark Matter Subhalos in Strong Gravitational Lensing with Neural Networks [pdf]
Joshua Yao-Yu Lin, Hang Yu, Warren Morningstar, Jian Peng and Gilbert Holder
130 Modular Deep Learning Analysis of Galaxy-Scale Strong Lensing Images [pdf]
Sandeep Madireddy, Nan Li, Nesar Ramachandra, Prasanna Balaprakash and Salman Habib
131 Graph Nets for Partial Charge Prediction [pdf]
Yuanqing Wang, Josh Fass, Chaya D. Stern, Kun Luo and John D. Chodera
132 Adiabatic Quantum Kitchen Sinks for Learning Kernels Using Randomized Features [pdf]
Moslem Noori, Seyed Shakib Vedaei, Inderpreet Singh, Daniel Crawford, Jaspreet S. Oberoi, Barry C. Sanders and Ehsan Zahedinejad
133 Wavelet-Powered Neural Networks for Turbulence [pdf]
Arvind Mohan, Daniel Livescu and Misha Chertkov
134 Site-specific graph neural network for predicting protonation energy of oxygenate molecules [pdf]
Romit Maulik, Rajeev Assary and Prasanna Balaprakash
135 Approaches for machine learning intermolecular interaction energies [pdf]
Derek Metcalf and David Sherrill
136 Prediction of GNSS Phase Scintillations: A Machine Learning Approach [pdf]
Kara Lamb, Garima Malhotra, Athanasios Vlontzos, Edward Wagstaff, Atılım Güneş Baydin, Anahita Bhiwandiwalla, Yarin Gal, Alfredo Kalaitzis, Anthony Reina and Asti Bhatt
137 Predicting Cosmological Massive Neutrino Simulation with Convolutional Neural Networks [pdf]
Elena Giusarma, Mauricio Reyes, Francisco Villaescusa-Navarro, Siyu He and Shirley Ho
138 Accurate Hydrologic Modeling Using Less Information [pdf]
Guy Shalev, Ran El-Yaniv, Daniel Klotz, Frederik Kratzert, Asher Metzger and Sella Nevo
139 Using Deep Siamese Neural Networks to Speed up Natural Products Research [pdf]
Nicholas Roberts, Poornav S. Purushothama, Vishal T. Vasudevan, Siddarth Ravichandran, Chen Zhang, William H. Gerwick and Garrison W. Cottrell
140 FaciesNet: Machine Learning Applications for Facies Classification in Well Logs [pdf]
Chayawan Jaikla, Pandu Devarakota, Neal Auchter, Mohamed Sidahmed and Irene Espejo
141 Biological Sequence Design using Batched Bayesian Optimization [pdf]
David Belanger, Suhani Vora, Zelda Mariet, Ramya Deshpande, David Dohan, Christof Angermueller, Kevin Murphy, Olivier Chapelle and Lucy Colwell
142 Accelerating Least Squares Imaging Using Deep Learning Techniques [pdf]
Janaki Vamaraju, Jeremy Vila, Mauricio Araya-Polo, Debanjan Datta, Mohamed Sidahmed and Mrinal Sen
143 Analysis of Chromatographic Data using the Probabilistic PARAFAC2 [pdf]
Philip Johan Havemann Jørgensen, Søren Føns Vind Nielsen, Jesper Løve Hinrich, Mikkel Nørgaard Schmidt, Kristoffer Hougaard Madsen and Morten Mørup
144 Classical Quantum Optimization with Neural Network Quantum States [pdf]
Joseph Gomes, Keri McKiernan, Peter Eastman and Vijay Pande
145 Partitioned integrators for thermodynamic parameterization of neural networks [pdf]
Tiffany Vlaar, Benedict Leimkuhler and Charles Matthews
146 Applying Machine Learning to Particle Track Identification in the L1-Trigger of the CMS Detector [pdf]
Claire Savard
147 Accounting for Physics Uncertainty in Ultrasonic Wave Propagation using Deep Learning [pdf]
Ishan D Khurjekar and Joel B. Harley
148 Learning Renormalization with a Convolutional Neural Network [pdf]
Kiel Howe and Alex Nguyen
149 A Conditional Generative Model for Predicting Material Microstructures from Processing Methods [pdf]
Akshay Iyer, Biswadip Dey, Arindam Dasgupta, Wei Chen and Amit Chakraborty
150 Anomalies detection with autoencoders [pdf]
Piotr Nowak
151 An Explainable Framework using Deep Attention Models for Sequential Data in Combustion Systems [pdf]
Tryambak Gangopadhyay, Sin Yong Tan, Anthony Locurto, James B. Michael and Soumik Sarkar
152 Deep Time Series Attention Models for Crop Yield Prediction and Insights [pdf]
Tryambak Gangopadhyay, Johnathon Shook, Asheesh K. Singh and Soumik Sarkar
153 Exploring the chemical space without bias: data-free molecule generation with DQN and SELFIES [pdf]
Théophile Gaudin, Akshat Nigam and Alan Aspuru-Guzik
154 Optimal Real-Space Renormalization-Group Transformations with Artificial Neural Networks [pdf]
Jui-Hui Chung and Ying-Jer Kao
155 Variational Autoencoders for Generative Modelling of Water Cherenkov Detectors [pdf]
Abhishek Abhishek, Wojciech Fedorko, Patrick de Perio, Julian Ding and Nicholas Prouse
156 Conservation Law Estimation by Extracting the Symmetry of a Dynamical System Using a DNN [pdf]
Yoh-Ichi Mototake
157 Quantum Graph Neural Networks [pdf]
Guillaume Verdon, Trevor McCourt, Stefan Leichenauer, Enxhell Luzhinca, Vikash Singh and Jack Hidary
158 Beyond Black-box Dictionary Learning for Waves [pdf]
Harsha Vardhan Tetali, K. Supreet Alguri and Joel Harley
159 Event Generation in Particle Physics with the B-VAE [pdf]
Sydney Otten, Shankha Banerjee, Melissa van Beekveld, Sascha Caron, Luc Hendriks, Caspar van Leeuwen, Damian Podareanu, Roberto Ruiz de Austri, Michael Spannowsky, Rob Verheyen and Philip Waite
160 Unsupervised Distribution Learning for Lunar Surface Anomaly Detection [pdf]
Adam Lesnikowski, Valentin Bickel and Daniel Angerhausen
161 Sparse Image Generation with Decoupled Generative Models [pdf]
Yadong Lu, Julian Collado, Kevin Bauer, Pierre Baldi and Daniel Whiteson

Program Committee (Reviewers)

We acknowledge the program committee for providing reviews on a very tight schedule (in alphabetical order):

Abigail Azari, Adi Hanuka, Aditi Krishnapriyan, Ahmed Mazari, Akella Ravi Tej, Aleksandr Berezutskii, Alessandra Tosi, Alex Beatson, Alex Rogozhnikov, Alexander Radovic, Alexander Schiendorfer, Alfredo Kalaitzis, Ali Tohidi, Alireza Sheikhattar, Amir Farbin, Amit Kumar Jaiswal, Andrey Prokopenko, Aneesh Rangnekar, Anirudh Som, Aranildo Lima, Arash Broumand, Arnab Bose, Arthur Pajot, Arthur Pesah, Arun Baskaran, Arya Farahi, Aseem Wadhwa, Ashish Mahabal, Ashok Vittal, Ashwin Balakrishna, Ata Mahjoubfar, Axel Sauer, Bao Nguyen T., Behrooz Mansouri, Benjamin Nachman, Biswadip Dey, Bohdan Kulchytskyy, Bradley Gram-Hansen, Bruce Bassett, Budhaditya Deb, Chase Shimmin, Chetan Tonde, Christopher Cramer, Christopher Tunnell, Cleber Zanchettin, Cory Stephenson, Craig Jones, Cristiano De Nobili, Daisuke Nagai, Dan Roberts, Daniel Bedau, Daniel Jiang, Daniel Roberts, Daniel Wagner Fonteles Alves, Daniel Worrall, David Betancourt, David Rousseau, David Shih, Devansh Agarwal, Dhagash Mehta, Diogo R. Ferreira, Donini Julien, Duncan Mcelfresh, Eftychios Pnevmatikakis, Elif Ozkirimli, Elijah Cole, Enrico Rinaldi, Eric Metodiev, Eric Nichols, Erick Moen, Erwan Allys, Evan Shellshear, Florian Schaefer, Francisco Villaescusa-Navarro, Frank Noe, Frank Soboczenski, Frederic Dreyer, Gabriella Contardo, Gavin Hartnett, Geoffrey Roeder, George Williams, Giancarlo Camilo, Giovanni De Gasperis, Giovanni Turra, Giuseppe Carleo, Giuseppe Castiglione, Grant Rotskoff, Guillaume Mahler, Gérard Dupont, Hao Wu, Haoran Liu, Haoxiang Wang, Harkirat Behl, Hasan Poonawala, Himaghna Bhattacharjee, Hossein Sharifi Noghabi, Jaan Altosaar, Jake Searcy, Jason Poulos, Jason Xiaotian Dou, Jean-Roch Vlimant, Jennifer Fernick, Jessica Forde, Jiequn Han, Jize Zhang, Joakim Andén, Johanna Hansen, John Pang, Jordan Hoffmann Hoffmann, Joshua Bloom, Juan Carrasquilla, Karthik Kashinath, Keiran Thompson, Kevin Yang, Kilian Koepsell, Kyle Cranmer, Lenka Zdeborova, Lu Lu, Luca Saglietti, Lucas Vinh Tran, Luke de Oliveira, Maghesree Chakraborty, Mahmood M. Shad, Marcel Schmittfull, Mariel Pettee, Mario Krenn, Matt Guttenberg, Matthew Beach, Matthew Feickert, Matthias Degroote, Maurizio Pierini, Maxwell Hutchinson, Mehmet Tan, Melanie Weber, Michael Albergo, Micky Paganini, Miles Cranmer, Mohamad Shahbazi, Mohammad Muneeb Sultan, Mustafa Mustafa, N. Benjamin Erichson, Nabeel Seedat, Naeemullah Khan, Nicholas Malaya, Nick Bhattacharya, Nick Litombe, Nicola Pancotti, Niranjan Sridhar, Nkosinathi Ndlovu, Olmo Cerri, Omar Jamil, Pablo de Castro Manzano, Patrick Kamongi, Peer-Timo Bremer, Peter Melchior, Philippe Dreesen, Prabhakar Marepalli, Prakash Mishra, Pranay Seshadri, Prannay Khosla, Pravallika Devineni, Praveen T N, Rachel Kurchin, Rajanie Prabha, Richard Feder, Robert Barton, Robert Zinkov, Roberto Bondesan, Robin Sandkuehler, Rodrigo Alejandro Vargas Hernández, Rogan Carr, Rushil Anirudh, Sadanand Singh, Saeed Seyyedi, Sahil Shah, Saleh Elmohamed, Samuel Yen-Chi Chen, Samujjwal Ghosh, Sandhya Prabhakaran, Sarah Marzen, Satpreet Singh, Saurabh Kumar, Savannah Thais, Sean Paradiso, Sebastian Goldt, Seungchan Kim, Sheng Liu, Shivam Saboo, Shivang Shekhar, Sho Yaida, Shubhendu Trivedi, Siddharth Mishra-Sharma, Simon Stieber, Sivaramakrishnan Swaminathan, Srikant Veeraraghavan, Stefan Krastanov, Stefano Sarao, Stefano Sarao Mannelli, Stephan Hoyer, Sucheta Jawalkar, Sydney Otten, Tal Kachman, Tan Nguyen, Tatiana Likhomanenko, Ted Hromadka, Theophile Gaudin, Thong Q. Nguyen, Thouis Jones, Tomo Lazovich, Tonio Buonassisi, Tsuyoshi Okita, Tzu-Chi Yen, Valentina Salvatelli, Venkat Viswanathan, Vladimir Milián Núñez, Vu Nguyen, Wahid Bhimji, Wanli Wu, William Shipman, Yangzesheng Sun, Yann Coadou, Yuefeng Zhang, Yury Tokpanov, Yves Mabiala, Zeeshan Ahmad, Zelong Zhang, Zhonghua Zheng, Zijian Hong


  • Vector Institute

  • Flatiron Institute


  • DeepMind



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