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