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Mon Dec 13 06:00 AM -- 03:30 PM (PST)
Machine Learning and the Physical Sciences
Anima Anandkumar · Kyle Cranmer · Mr. Prabhat · Lenka Zdeborová · Atilim Gunes Baydin · Juan Carrasquilla · Emine Kucukbenli · Gilles Louppe · Benjamin Nachman · Brian Nord · Savannah Thais

Workshop Home Page

The "Machine Learning and the Physical Sciences" workshop aims to provide a cutting-edge venue for research at the interface of machine learning (ML) and the physical sciences. This interface spans (1) applications of ML in physical sciences (“ML for physics”) and (2) developments in ML motivated by physical insights (“physics for ML”).

ML 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 ML 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 ML models for scientific discovery, tools and insights from the physical sciences are increasingly brought to the study of ML models.

By bringing together ML researchers and physical scientists who apply and study ML, 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, which will also include contributed talks selected from submissions. The workshop will also feature an expert panel discussion on “Physics for ML" and a breakout session dedicated to community building will serve to foster dialogue between physical science and ML research communities.

Session 1 | Opening remarks (Live intro)
Session 1 | Invited talk: Max Welling, "Accelerating simulations of nature, both classical and quantum, with equivariant deep learning" (Invited talk (live))
Session 1 | Invited talk Q&A: Max Welling (Live Q&A)
Session 1 | Invited talk: Bingqing Cheng, "Predicting material properties with the help of machine learning" (Invited talk (live))
Session 1 | Invited talk Q&A: Bingqing Cheng (Live Q&A)
Session 1 | Contributed talk: Tian Xie, "Crystal Diffusion Variational Autoencoder for Periodic Material Generation" (Contributed talk (live))
Session 1 | Poster session (Poster session (
Session 2 | Opening remarks (Live intro)
Session 2 | Panel discussion: Jennifer Chayes, Marylou Gabrié, Michela Paganini, Sara Solla, Moderator: Lenka Zdeborová (Live panel discussion)
Session 2 | Invited talk: Megan Ansdell, "NASA's efforts & opportunities to support ML in the Physical Sciences" (Invited talk (live))
Session 2 | Invited talk Q&A: Megan Ansdell (Live Q&A)
Session 2 | Contributed talk: George Stein, "Self-supervised similarity search for large scientific datasets" (Contributed talk (live))
Session 2 | Poster session (Poster session (
Session 3 | Opening remarks (Live intro)
Session 3 | Invited talk: Surya Ganguli, "From the geometry of high dimensional energy landscapes to optimal annealing in a dissipative many body quantum optimizer" (Invited talk (live))
Session 3 | Invited talk Q&A: Surya Ganguli (Live Q&A)
Session 3 | Invited talk: Laure Zanna, "The future of climate modeling in the age of machine learning" (Invited talk (live))
Session 3 | Invited talk Q&A: Laure Zanna (Live Q&A)
Session 3 | Contributed talk: Maximilian Dax, "Amortized Bayesian inference of gravitational waves with normalizing flows" (Contributed talk (live))
Session 3 | Community development breakouts (Community breakout session (
Session 3 | Feedback from community development breakouts and closing remarks (Live feedback)
Deep learning techniques for a real-time neutrino classifier (Poster)
Deep learning reconstruction of the neutrino energy with a shallow Askaryan detector (Poster)
Inferring dark matter substructure with global astrometry beyond the power spectrum (Poster)
Stochastic Adversarial Koopman Model for Dynamical Systems (Poster)
Discovering PDEs from Multiple Experiments (Poster)
Nonlinear pile-up separation with LSTM neural networks for cryogenic particle detectors (Poster)
Machine learning accelerated particle-in-cell plasma simulations (Poster)
Calibrating Electrons and Photons in the CMS ECAL using Graph Neural Networks (Poster)
Re-calibrating Photometric Redshift Probability Distributions Using Feature-space Regression (Poster)
Towards Improved Global River Discharge Prediction in Ungauged Basins Using Machine Learning and Satellite Observations (Poster)
Visualization of nonlinear modal structures for three-dimensional unsteady fluid flows with customized decoder design (Poster)
Sharpness-Aware Minimization for Robust Molecular Dynamics Simulations (Poster)
A posteriori learning for quasi-geostrophic turbulence parametrization (Poster)
Vision transformers and techniques for improving solar wind speed forecasts using solar EUV images (Poster)
Deep-DFT: Physics-ML hybrid method to predict DFT energy using Transformer (Poster)
Fine-tuning Vision Transformers for the Prediction of State Variables in Ising Models (Poster)
Physics-informed neural network for inversely predicting effective electric permittivities of metamaterials (Poster)
Learning Size and Shape of Calabi-Yau Spaces (Poster)
Learning Full Configuration Interaction Electron Correlations with Deep Learning (Poster)
A deep ensemble approach to X-ray polarimetry (Poster)
Learning Discrete Neural Reaction Class to Improve Retrosynthesis Prediction (Poster)
Classical variational simulation of the Quantum Approximate Optimization Algorithm (Poster)
Deep Surrogate for Direct Time Fluid Dynamics (Poster)
Normalizing Flows for Random Fields in Cosmology (Poster)
Machine Learning and Dynamical Models for Sub-seasonal Climate Forecasting (Poster)
Multiway Ensemble Kalman Filter (Poster)
Neural quantum states for supersymmetric quantum gauge theories (Poster)
A Quasi-Universal Neural Network to Model Structure Formation in the Universe (Poster)
Learning governing equations of interacting particle systems using Gaussian process regression (Poster)
Marrying the benefits of Automatic and Numerical Differentiation in Physics-Informed Neural Network (Poster)
G-SpaNet: Generalized Permutationless Set Assignment for Particle Physics using Symmetry Preserving Attention (Poster)
Critical parametric quantum sensing with machine learning (Poster)
Scaling Up Machine Learning For Quantum Field Theory with Equivariant Continuous Flows (Poster)
Equivariant Transformers for Neural Network based Molecular Potentials (Poster)
Fast Approximate Model for the 3D Matter Power Spectrum (Poster)
3D Pre-training improves GNNs for Molecular Property Prediction (Poster)
Learning the exchange-correlation functional from nature with differentiable density functional theory (Poster)
RBSRICNN: Raw Burst Super-Resolution through Iterative Convolutional Neural Network (Poster)
Classifying Anomalies THrough Outer Density Estimation (CATHODE) (Poster)
Unsupervised topological learning approach of crystal nucleation in pure Tantalum (Poster)
DeepBO: Deep Neural-Network Bayesian Optimization of Polaritonic Metasurfaces in Continuous Space (Poster)
A data-driven wall model for the prediction of turbulent flow separation over periodic hills (Poster)
Extending turbulence model uncertainty quantification using machine learning (Poster)
Missing Data Imputation for Galaxy Redshift Estimation (Poster)
Arbitrary Marginal Neural Ratio Estimation for Simulation-based Inference (Poster)
Amortized Bayesian inference of gravitational waves with normalizing flows (Poster)
Weight Pruning and Uncertainty in Radio Galaxy Classification (Poster)
Automatic differentiation approach for reconstructing spectral functions with neural networks (Poster)
Characterizing γ-ray maps of the Galactic Center with neural density estimation (Poster)
Bayesian Stokes inversion with Normalizing flows (Poster)
Generative models for hadron shower simulation (Poster)
Detecting Spatiotemporal Lightning Patterns: An Unsupervised Graph-Based Approach (Poster)
Uncertainty Aware Learning for High Energy Physics With A Cautionary Tale (Poster)
Error Analysis of Kilonova Surrogate Models (Poster)
Can semi-supervised learning reduce the amount of manual labelling required for effective radio galaxy morphology classification? (Poster)
A simple equivariant machine learning method for dynamics based on scalars (Poster)
Physics-enhanced Neural Networks in the Small Data Regime (Poster)
Approximate Latent Force Model Inference (Poster)
Rethinking the modeling of the instrumental response of telescopes with a differentiable optical model (Poster)
Inorganic Synthesis Reaction Condition Prediction with Generative Machine Learning (Poster)
Quantum Machine Learning for Radio Astronomy (Poster)
Proximal Biasing for Bayesian Optimization and Characterization of Physical Systems (Poster)
Learning the solar latent space: sigma-variational autoencoders for multiple channel solar imaging (Poster)
Digital Twin Earth - Coasts: Developing a fast and physics-informed surrogate model for coastal floods via neural operators (Poster)
Probabilistic neural networks for predicting energy dissipation rates in geophysical turbulent flows (Poster)
Robust and Provably Monotonic Networks (Poster)
Predicting flux in Discrete Fracture Networks via Graph Informed Neural Networks (Poster)
Stronger symbolic summary statistics for the LHC (Poster)
Photometric Redshifts for Cosmology: Improving Accuracy and Uncertainty Estimates Using Bayesian Neural Networks (Poster)
Symmetries and self-supervision in particle physics (Poster)
Cooperative multi-agent reinforcement learning outperforms decentralized execution in high-dimensional nonequilibrium control for steady-state design (Poster)
Supplementing Recurrent Neural Network Wave Functions with Symmetry and Annealing to Improve Accuracy (Poster)
An Imperfect machine to search for New Physics: systematic uncertainties in a machine-learning based signal extraction (Poster)
Optimizing High-Dimensional Physics Simulations via Composite Bayesian Optimization (Poster)
Simulation-Based Inference of Strong Gravitational Lensing Parameters (Poster)
Out of equilibrium learning dynamics in physical allosteric resistor networks (Poster)
A New sPHENIX Heavy Quark Trigger Algorithm Based on Graph Neutral Networks (Poster)
Particle Graph Autoencoders and Differentiable, Learned Energy Mover's Distance (Poster)
Rethinking Neural Networks with Benford's Law (Poster)
A Convolutional Autoencoder-Based Pipeline For Anomaly Detection And Classification Of Periodic Variables (Poster)
Partial-Attribution Instance Segmentation for Astronomical Source Detection and Deblending (Poster)
Electromagnetic Counterpart Identification of Gravitational-wave candidates using deep-learning (Poster)
Cross-Modal Virtual Sensing for Combustion Instability Monitoring (Poster)
PlasmaNet: a framework to study and solve elliptic differential equations using neural networks in plasma fluid simulations (Poster)
Equivariant and Modular DeepSets with Applications in Cluster Cosmology (Poster)
A debiasing framework for deep learning applied to the morphological classification of galaxies (Poster)
The Quantum Trellis: A classical algorithm for sampling the parton shower with interference effects (Poster)
Variational framework for partially-measured physical system control (Poster)
An ML Framework for Estimating Bayesian Posteriors of Galaxy Morphological Parameters (Poster)
Analyzing High-Resolution Clouds and Convection using Multi-Channel VAEs (Poster)
Detecting Low Surface Brightness Galaxies with Mask R-CNN (Poster)
Online Bayesian Optimization for Beam Alignment in the SECAR Recoil Mass Separator (Poster)
Implicit Quantile Neural Networks for Jet Simulation and Correction (Poster)
Real-time Detection of Anomalies in Multivariate Time Series of Astronomical Data (Poster)
Neural network is heterogeneous: Phase matters more (Poster)
Simultaneous Multivariate Forecast of Space Weather Indices using Deep Neural Network Ensembles (Poster)
Symmetry Discovery with Deep Learning (Poster)
Approximate Bayesian Computation for Physical Inverse Modeling (Poster)
Noether Networks: Meta-Learning Useful Conserved Quantities (Poster)
Explaining machine-learned particle-flow reconstruction (Poster)
Physics Informed RNN-DCT Networks for Time-Dependent Partial Differential Equations (Poster)
A Granular Method for Finding Anomalous Light Curves and their Analogs (Poster)
Galaxy Morphological Classification with Efficient Vision Transformer (Poster)
End-To-End Online sPHENIX Trigger Detection Pipeline (Poster)
Accelerator Tuning with Deep Reinforcement Learning (Poster)
Geometric Priors for Scientific Generative Models in Inertial Confinement Fusion (Poster)
Neural Tensor Contractions and the Expressive Power of Deep Neural Quantum States (Poster)
Fast synthesis and inversion of spectral lines in stellar chromospheres with graph networks (Poster)
Embedding temporal error propagation on CNN for unsteady flow simulations (Poster)
Phenomenological classification of the Zwicky Transient Facility astronomical event alerts (Poster)
An Emulation Framework for Fire Front Spread (Poster)
Score-based Graph Generative Model for Neutrino Events Classification and Reconstruction (Poster)
E(2) Equivariant Self-Attention for Radio Astronomy (Poster)
Unbiased Monte Carlo Cluster Updates with Autoregressive Neural Networks (Poster)
Using Deep Learning for estimation of river surface elevation from photogrammetric Digital Surface Models (Poster)
Crystal graph convolutional neural networks for per-site property prediction (Poster)
Differentiable Strong Lensing for Complex Lens Modelling (Poster)
Probing the Structure of String Theory Vacua with Genetic Algorithms and Reinforcement Learning (Poster)
Deep-SWIM: A few-shot learning approach to classify Solar WInd Magnetic field structures (Poster)
Factorized Fourier Neural Operators (Poster)
Equivariant graph neural networks as surrogate for computational fluid dynamics in 3D artery models (Poster)
Probabilistic segmentation of overlapping galaxies for large cosmological surveys. (Poster)
Turbo-Sim: a generalised generative model with a physical latent space (Poster)
Deterministic particle flows for constraining SDEs (Poster)
Graph Segmentation in Scientific Datasets (Poster)
CaloDVAE : Discrete Variational Autoencoders for Fast Calorimeter Shower Simulation (Poster)
Efficient kernel methods for model-independent new physics searches (Poster)
Analysis of ODE2VAE with Examples (Poster)
Rethinking Graph Transformers with Spectral Attention (Poster)
Self-supervised similarity search for large scientific datasets (Poster)
Crystal Diffusion Variational Autoencoder for Periodic Material Generation (Poster)
Flood Segmentation on Sentinel-1 SAR Imagery with Semi-Supervised Learning (Poster)
A General Method for Calibrating Stochastic Radio Channel Models with Kernels (Poster)
A Multi-Survey Dataset and Benchmark for First Break Picking in Hard Rock Seismic Exploration (Poster)
Using Mask R-CNN to detect and mask ghosting and scattered-light artifacts in astronomical images (Poster)
Neural density estimation and uncertainty quantification for laser induced breakdown spectroscopy spectra (Poster)
Robustness of deep learning algorithms in astronomy - galaxy morphology studies (Poster)
Uncertainty quantification for ptychography using normalizing flows (Poster)
Model Inversion for Spatio-temporal Processes using the Fourier Neural Operator (Poster)
Mixture-of-Experts Ensemble with Hierarchical Deep Metric Learning for Spectroscopic Identification (Poster)
Amortized Variational Inference for Type Ia Supernova Light Curves (Poster)
Scalable Bayesian Optimization Accelerates Process Optimization of Penicillin Production (Poster)
Neural Symplectic Integrator with Hamiltonian Inductive Bias for the Gravitational N-body Problem (Poster)
Using neural networks to reduce communication in numerical solution of partial differential equations (Poster)
Modeling Advection on Directed Graphs using Mat\'{e}rn Gaussian Processes for Traffic Flow (Poster)
Unsupervised Spectral Unmixing for Telluric Correction using a Neural Network Autoencoder (Poster)
S3RP: Self-Supervised Super-Resolution and Prediction for Advection-Diffusion Process (Poster)
Automatically detecting anomalous exoplanet transits (Poster)
DeepZipper: A Novel Deep Learning Architecture for Lensed Supernovae Identification (Poster)
Using physics-informed regularization to improve extrapolation capabilities of neural networks (Poster)
Single Image Super-Resolution with Uncertainty Estimation for Lunar Satellite Images (Poster)