Workshop
Machine Learning and the Physical Sciences
Atilim Gunes Baydin · Adji Bousso Dieng · Emine Kucukbenli · Gilles Louppe · Siddharth Mishra-Sharma · Benjamin Nachman · Brian Nord · Savannah Thais · Anima Anandkumar · Kyle Cranmer · Lenka Zdeborová · Rianne van den Berg
Room 275 - 277
Sat 3 Dec, 5:50 a.m. PST
The Machine Learning and the Physical Sciences workshop aims to provide an informal, inclusive and leading-edge venue for research and discussions at the interface of machine learning (ML) and the physical sciences. This interface spans (1) applications of ML in physical sciences (ML for physics), (2) developments in ML motivated by physical insights (physics for ML), and most recently (3) convergence of ML and physical sciences (physics with ML) which inspires questioning what scientific understanding means in the age of complex-AI powered science, and what roles machine and human scientists will play in developing scientific understanding in the future.
Chat is not available.
Timezone: America/Los_Angeles
Schedule
|
Sat 5:50 a.m. - 6:00 a.m.
|
Opening remarks
(
Introduction to the Workshop
)
>
SlidesLive Video |
🔗 |
|
Sat 6:00 a.m. - 6:30 a.m.
|
Invited talk: David Pfau, "Deep Learning and Ab-Initio Quantum Chemistry and Materials"
(
Invited talk
)
>
SlidesLive Video |
David Pfau · Siddharth Mishra-Sharma 🔗 |
|
Sat 6:30 a.m. - 6:45 a.m.
|
Contributed talk: Kieran Murphy, "Characterizing information loss in a chaotic double pendulum with the Information Bottleneck"
(
Contributed talk
)
>
SlidesLive Video |
Kieran Murphy · Siddharth Mishra-Sharma 🔗 |
|
Sat 6:45 a.m. - 7:15 a.m.
|
Invited talk: Hiranya Peiris, "Prospects for understanding the physics of the Universe"
(
Invited talk
)
>
SlidesLive Video |
Hiranya Peiris · Siddharth Mishra-Sharma 🔗 |
|
Sat 7:15 a.m. - 7:30 a.m.
|
Contributed talk: Marco Aversa, "Physical Data Models in Machine Learning Imaging Pipelines"
(
Contributed talk
)
>
SlidesLive Video |
Marco Aversa · Siddharth Mishra-Sharma 🔗 |
|
Sat 7:30 a.m. - 8:00 a.m.
|
Invited Talk: Giorgio Parisi
(
Invited talk
)
>
SlidesLive Video |
🔗 |
|
Sat 8:00 a.m. - 9:00 a.m.
|
Poster session 1 and break ( Poster session and break ) > link | 🔗 |
|
Sat 9:00 a.m. - 10:00 a.m.
|
Panel: Kathleen Creel, Mario Krenn, and Emily Sullivan, "Philosophy of Science in the AI Era"
(
Panel
)
>
SlidesLive Video |
🔗 |
|
Sat 10:00 a.m. - 11:15 a.m.
|
Lunch
(
Lunch
)
>
|
🔗 |
|
Sat 11:15 a.m. - 11:45 a.m.
|
Invited talk: E. Doğuş Çubuk, "Scaling up material discovery via deep learning"
(
Invited talk
)
>
SlidesLive Video |
Ekin Dogus Cubuk · Siddharth Mishra-Sharma 🔗 |
|
Sat 11:45 a.m. - 12:15 p.m.
|
Invited talk: Vinicius Mikuni, "Collider Physics Innovations Powered by Machine Learning"
(
Invited talk
)
>
SlidesLive Video |
Vinicius Mikuni · Siddharth Mishra-Sharma 🔗 |
|
Sat 12:15 p.m. - 12:30 p.m.
|
Contributed talk: Aurélien Dersy, "Simplifying Polylogarithms with Machine Learning"
(
Contributed talk
)
>
SlidesLive Video |
Aurelien Dersy · Siddharth Mishra-Sharma 🔗 |
|
Sat 12:30 p.m. - 1:00 p.m.
|
Invited talk: Federico Felici, "Magnetic control of tokamak plasmas through Deep Reinforcement Learning"
(
Invited talk
)
>
SlidesLive Video |
Federico Felici · Siddharth Mishra-Sharma 🔗 |
|
Sat 1:00 p.m. - 1:15 p.m.
|
Contributed talk: Alexandre Adam, "Posterior samples of source galaxies in strong gravitational lenses with score-based priors"
(
Contributed talk
)
>
SlidesLive Video |
Alexandre Adam · Siddharth Mishra-Sharma 🔗 |
|
Sat 1:15 p.m. - 1:30 p.m.
|
Break
|
🔗 |
|
Sat 1:30 p.m. - 2:00 p.m.
|
Invited talk: Catherine Nakalembe and Hannah Kerner
(
Invited talk
)
>
SlidesLive Video |
Catherine Nakalembe · Hannah Kerner · Siddharth Mishra-Sharma 🔗 |
|
Sat 2:00 p.m. - 2:05 p.m.
|
Closing remarks
(
Closing remarks
)
>
|
🔗 |
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Sat 2:05 p.m. - 3:00 p.m.
|
Poster session 2 ( Poster session ) > link | 🔗 |
|
-
|
Leveraging the Stochastic Predictions of Bayesian Neural Networks for Fluid Simulations
(
Poster
)
>
|
Maximilian Mueller · Robin Greif · Frank Jenko · Nils Thuerey 🔗 |
|
-
|
Discovering Long-period Exoplanets using Deep Learning with Citizen Science Labels
(
Poster
)
>
|
Shreshth A Malik · Nora Eisner · Chris Lintott · Yarin Gal 🔗 |
|
-
|
HIGlow: Conditional Normalizing Flows for High-Fidelity HI Map Modeling
(
Poster
)
>
|
Roy Friedman · Sultan Hassan 🔗 |
|
-
|
Virgo: Scalable Unsupervised Classification of Cosmological Shock Waves
(
Poster
)
>
|
Max Lamparth · Ludwig Böss · Ulrich Steinwandel · Klaus Dolag 🔗 |
|
-
|
Learning Feynman Diagrams using Graph Neural Networks
(
Poster
)
>
|
Alexander Norcliffe · Harrison Mitchell · Pietro Lió 🔗 |
|
-
|
Certified data-driven physics-informed greedy auto-encoder simulator
(
Poster
)
>
|
Xiaolong He · Youngsoo Choi · William Fries · Jon Belof · Jiun-Shyan Chen 🔗 |
|
-
|
Physics-Informed Machine Learning of Dynamical Systems for Efficient Bayesian Inference
(
Poster
)
>
|
Som Dhulipala · Yifeng Che · Michael Shields 🔗 |
|
-
|
Offline Model-Based Reinforcement Learning for Tokamak Control
(
Poster
)
>
|
11 presentersIan Char · Joseph Abbate · Laszlo Bardoczi · Mark Boyer · Youngseog Chung · Rory Conlin · Keith Erickson · Viraj Mehta · Nathan Richner · Egemen Kolemen · Jeff Schneider |
|
-
|
Decay-aware neural network for event classification in collider physics
(
Poster
)
>
|
Tomoe Kishimoto · Masahiro Morinaga · Masahiko Saito · Junichi Tanaka 🔗 |
|
-
|
Phase transitions and structure formation in learning local rules
(
Poster
)
>
|
Bojan Žunkovič · Enej Ilievski 🔗 |
|
-
|
Lyapunov Regularized Forecaster
(
Poster
)
>
|
Rong Zheng · Rose Yu 🔗 |
|
-
|
Ad-hoc Pulse Shape Simulation using Cyclic Positional U-Net
(
Poster
)
>
|
Aobo Li 🔗 |
|
-
|
Learning Uncertainties the Frequentist Way: Calibration and Correlation in High Energy Physics
(
Poster
)
>
|
Rikab Gambhir · Jesse Thaler · Benjamin Nachman 🔗 |
|
-
|
Molecular Fingerprints for Robust and Efficient ML-Driven Molecular Generation
(
Poster
)
>
|
Ruslan Tazhigulov · Joshua Schiller · Jacob Oppenheim · Max Winston 🔗 |
|
-
|
Machine Learning for Chemical Reactions \\A Dance of Datasets and Models
(
Poster
)
>
|
Mathias Schreiner · Arghya Bhowmik · Tejs Vegge · Jonas Busk · Peter Bjørn Jørgensen · Ole Winther 🔗 |
|
-
|
ML4LM: Machine Learning for Safely Landing on Mars
(
Poster
)
>
|
David Wu · Wai Tong Chung · Matthias Ihme 🔗 |
|
-
|
Flexible learning of quantum states with generative query neural networks
(
Poster
)
>
|
Yan Zhu · Ya-Dong Wu · Ge Bai · Dong-Sheng Wang · Yuexuan Wang · Giulio Chiribella 🔗 |
|
-
|
Transfer Learning with Physics-Informed Neural Networks for Efficient Simulation of Branched Flows
(
Poster
)
>
|
Raphael Pellegrin · Blake Bullwinkel · Marios Mattheakis · Pavlos Protopapas 🔗 |
|
-
|
Decorrelation with Conditional Normalizing Flows
(
Poster
)
>
|
Samuel Klein · Tobias Golling 🔗 |
|
-
|
A New Task: Deriving Semantic Class Targets for the Physical Sciences
(
Poster
)
>
|
Micah Bowles 🔗 |
|
-
|
Machine-learned climate model corrections from a global storm-resolving model
(
Poster
)
>
|
Anna Kwa 🔗 |
|
-
|
Amortized Bayesian Inference for Supernovae in the Era of the Vera Rubin Observatory Using Normalizing Flows
(
Poster
)
>
|
Victoria Villar 🔗 |
|
-
|
Scalable Bayesian Inference for Finding Strong Gravitational Lenses
(
Poster
)
>
|
Yash Patel · Jeffrey Regier 🔗 |
|
-
|
Training physical networks like neural networks: deep physical neural networks
(
Poster
)
>
|
Logan Wright · Tatsuhiro Onodera · Martin M Stein · Tianyu Wang · Darren Schachter · Zoey Hu · Peter McMahon 🔗 |
|
-
|
A Curriculum-Training-Based Strategy for Distributing Collocation Points during Physics-Informed Neural Network Training
(
Poster
)
>
|
Marcus Münzer · Christopher Bard 🔗 |
|
-
|
Learning latent variable evolution for the functional renormalization group
(
Poster
)
>
|
Matija Medvidović · Alessandro Toschi · Giorgio Sangiovanni · Cesare Franchini · Andy Millis · Anirvan Sengupta · Domenico Di Sante 🔗 |
|
-
|
Deformations of Boltzmann Distributions
(
Poster
)
>
|
Bálint Máté · François Fleuret 🔗 |
|
-
|
Neuro-Symbolic Partial Differential Equation Solver
(
Poster
)
>
|
Pouria Akbari Mistani · Samira Pakravan · Rajesh Ilango · Sanjay Choudhry · Frederic Gibou 🔗 |
|
-
|
Generating astronomical spectra from photometry with conditional diffusion models
(
Poster
)
>
|
Lars Doorenbos · Stefano Cavuoti · Giuseppe Longo · Massimo Brescia · Raphael Sznitman · Pablo Márquez Neila 🔗 |
|
-
|
Identifying AGN host galaxies with convolutional neural networks
(
Poster
)
>
|
Ziting Guo · John Wu · Chelsea Sharon 🔗 |
|
-
|
Efficiently Moving Instead of Reweighting Collider Events with Machine Learning
(
Poster
)
>
|
Radha Mastandrea · Benjamin Nachman 🔗 |
|
-
|
D-optimal neural exploration of nonlinear physical systems
(
Poster
)
>
|
Matthieu Blanke · marc lelarge 🔗 |
|
-
|
Machine learning for complete intersection Calabi-Yau manifolds
(
Poster
)
>
|
Harold Erbin · Mohamed Tamaazousti · Riccardo Finotello 🔗 |
|
-
|
SuNeRF: Validation of a 3D Global Reconstruction of the Solar Corona Using Simulated EUV Images
(
Poster
)
>
|
11 presentersKyriaki-Margarita Bintsi · Robert Jarolim · Benoit Tremblay · Miraflor Santos · Anna Jungbluth · James Mason · Sairam Sundaresan · Angelos Vourlidas · Cooper Downs · Ronald Caplan · Andres Munoz-Jaramillo |
|
-
|
Generating Calorimeter Showers as Point Clouds
(
Poster
)
>
|
Simon Schnake · Dirk Krücker · Kerstin Borras 🔗 |
|
-
|
Physics solutions for privacy leaks in machine learning
(
Poster
)
>
|
Alejandro Pozas-Kerstjens · Senaida Hernandez-Santana · José Ramón Pareja Monturiol · Marco Castrillon Lopez · Giannicola Scarpa · Carlos E. Gonzalez-Guillen · David Perez-Garcia 🔗 |
|
-
|
From Particles to Fluids: Dimensionality Reduction for Non-Maxwellian Plasma Velocity Distributions Validated in the Fluid Context
(
Poster
)
>
|
Daniel da Silva 🔗 |
|
-
|
Simplifying Polylogarithms with Machine Learning
(
Poster
)
>
|
Aurelien Dersy · Matthew Schwartz · Xiaoyuan Zhang 🔗 |
|
-
|
NLP Inspired Training Mechanics For Modeling Transient Dynamics
(
Poster
)
>
|
Lalit Ghule · Rishikesh Ranade · Jay Pathak 🔗 |
|
-
|
Neural Network-based Real-Time Parameter Estimation in Electrochemical Sensors with Unknown Confounding Factors
(
Poster
)
>
|
Sarthak Jariwala 🔗 |
|
-
|
Learning dynamical systems: an example from open quantum system dynamics.
(
Poster
)
>
|
Pietro Novelli 🔗 |
|
-
|
Reducing Down(stream)time: Pretraining Molecular GNNs using Heterogeneous AI Accelerators
(
Poster
)
>
|
Jenna A Bilbrey · Kristina Herman · Henry Sprueill · Sotiris Xantheas · Payel Das · Manuel Lopez Roldan · Mike Kraus · Hatem Helal · Sutanay Choudhury 🔗 |
|
-
|
Emulating Fast Processes in Climate Models
(
Poster
)
>
|
Noah Brenowitz · W. Andre Perkins · Jacqueline M. Nugent · Oliver Watt-Meyer · Spencer K. Clark · Anna Kwa · Brian Henn · Jeremy McGibbon · Christopher S. Bretherton 🔗 |
|
-
|
GAUCHE: A Library for Gaussian Processes in Chemistry
(
Poster
)
>
|
18 presentersRyan-Rhys Griffiths · Leo Klarner · Henry Moss · Aditya Ravuri · Sang Truong · Bojana Rankovic · Yuanqi Du · Arian Jamasb · Julius Schwartz · Austin Tripp · Gregory Kell · Anthony Bourached · Alex Chan · Jacob Moss · Chengzhi Guo · Alpha Lee · Philippe Schwaller · Jian Tang |
|
-
|
One-shot learning for solution operators of partial differential equations
(
Poster
)
>
|
Lu Lu · Anran Jiao · Jay Pathak · Rishikesh Ranade · Haiyang He 🔗 |
|
-
|
Wavelets Beat Monkeys at Adversarial Robustness
(
Poster
)
>
|
Jingtong Su · Julia Kempe 🔗 |
|
-
|
Qubit seriation: Undoing data shuffling using spectral ordering
(
Poster
)
>
|
Atithi Acharya · Manuel Rudolph · Jing Chen · Jacob Miller · Alejandro Perdemo-Ortiz 🔗 |
|
-
|
First principles physics-informed neural network for quantum wavefunctions and eigenvalue surfaces
(
Poster
)
>
|
Marios Mattheakis · Gabriel R. Schleder · Daniel Larson · Efthimios Kaxiras 🔗 |
|
-
|
Clustering Behaviour of Physics-Informed Neural Networks: Inverse Modeling of An Idealized Ice Shelf
(
Poster
)
>
|
Yunona Iwasaki · Ching-Yao Lai 🔗 |
|
-
|
Renormalization in the neural network-quantum field theory correspondence
(
Poster
)
>
|
Harold Erbin · Vincent Lahoche · Dine Ousmane Samary 🔗 |
|
-
|
Applying Deep Reinforcement Learning to the HP Model for Protein Structure Prediction
(
Poster
)
>
|
Kaiyuan Yang · Houjing Huang · Olafs Vandans · Adithyavairavan Murali · Fujia Tian · Roland Yap · Liang Dai 🔗 |
|
-
|
Intra-Event Aware Imitation Game for Fast Detector Simulation
(
Poster
)
>
|
Hosein Hashemi · Nikolai Hartmann · Sahand Sharifzadeh · James Kahn · Thomas Kuhr 🔗 |
|
-
|
Deep Learning Modeling of Subgrid Physics in Cosmological N-body Simulations
(
Poster
)
>
|
Georgios Markos Chatziloizos · Francois Lanusse · Tristan Cazenave 🔗 |
|
-
|
Combinational-convolution for flow-based sampling algorithm
(
Poster
)
>
|
Akio Tomiya 🔗 |
|
-
|
Point Cloud Generation using Transformer Encoders and Normalising Flows
(
Poster
)
>
|
Benno Käch · Dirk Krücker · Isabell Melzer 🔗 |
|
-
|
Learning Similarity Metrics for Volumetric Simulations with Multiscale CNNs
(
Poster
)
>
|
Georg Kohl · Liwei Chen · Nils Thuerey 🔗 |
|
-
|
Stabilization and Acceleration of CFD Simulation by Controlling Relaxation Factor Based on Residues: An SNN Based Approach
(
Poster
)
>
|
Sounak Dey · Dighanchal Banerjee · Mithilesh Maurya · Dilshad Ahmad 🔗 |
|
-
|
Simulation-based inference of the 2D ex-situ stellar mass fraction distribution of galaxies using variational autoencoders
(
Poster
)
>
|
Eirini Angeloudi · Marc Huertas-Company · Jesús Falcón-Barroso · Regina Sarmiento · Daniel Walo-Martín · Annalisa Pillepich · Jesús Vega Ferrero 🔗 |
|
-
|
Uncertainty quantification methods for ML-based surrogate models of scientific applications
(
Poster
)
>
|
Kishore Basu · Yujia Hao · Delphine Hintz · Dev Shah · Aaron Palmer · Gurpreet Singh Hora · Darian Nwankwo · Laurent White 🔗 |
|
-
|
Contrasting random and learned features in deep Bayesian linear regression
(
Poster
)
>
|
Jacob Zavatone-Veth · William Tong · Cengiz Pehlevan 🔗 |
|
-
|
DS-GPS : A Deep Statistical Graph Poisson Solver (for faster CFD simulations)
(
Poster
)
>
|
Matthieu Nastorg 🔗 |
|
-
|
Dynamical Mean Field Theory of Kernel Evolution in Wide Neural Networks
(
Poster
)
>
|
Blake Bordelon · Cengiz Pehlevan 🔗 |
|
-
|
Semi-Supervised Domain Adaptation for Cross-Survey Galaxy Morphology Classification and Anomaly Detection
(
Poster
)
>
|
Aleksandra Ciprijanovic · Ashia Lewis · Kevin Pedro · Sandeep Madireddy · Brian Nord · Gabriel Nathan Perdue · Stefan Wild 🔗 |
|
-
|
A Neural Network Subgrid Model of the Early Stages of Planet Formation
(
Poster
)
>
|
Thomas Pfeil · Miles Cranmer · Shirley Ho · Philip Armitage · Tilman Birnstiel · Hubert Klahr 🔗 |
|
-
|
Validation Diagnostics for SBI algorithms based on Normalizing Flows
(
Poster
)
>
|
Julia Linhart · Alexandre Gramfort · Pedro Rodrigues 🔗 |
|
-
|
One Network to Approximate Them All: Amortized Variational Inference of Ising Ground States
(
Poster
)
>
|
Sebastian Sanokowski · Wilhelm Berghammer · Johannes Kofler · Sepp Hochreiter · Sebastian Lehner 🔗 |
|
-
|
Hybrid integration of the gravitational N-body problem with Artificial Neural Networks
(
Poster
)
>
|
Veronica Saz Ulibarrena · Simon Portegies Zwart · Elena Sellentin · Barry Koren · Philipp Horn · Maxwell X. Cai 🔗 |
|
-
|
CAPE: Channel-Attention-Based PDE Parameter Embeddings for SciML
(
Poster
)
>
|
Makoto Takamoto · Francesco Alesiani · Mathias Niepert 🔗 |
|
-
|
Real-time Health Monitoring of Heat Exchangers using Hypernetworks and PINNs
(
Poster
)
>
|
Ritam Majumdar · Vishal Jadhav · Anirudh Deodhar · Shirish Karande · Lovekesh Vig · Venkataramana Runkana 🔗 |
|
-
|
Physics-Informed CNNs for Super-Resolution of Sparse Observations on Dynamical Systems
(
Poster
)
>
|
Daniel Kelshaw · Georgios Rigas · Luca Magri 🔗 |
|
-
|
Neural Inference of Gaussian Processes for Time Series Data of Quasars
(
Poster
)
>
|
Egor Danilov · Aleksandra Ciprijanovic · Brian Nord 🔗 |
|
-
|
Deep Learning-Based Spatiotemporal Multi-Event Reconstruction for Delay-Line Detectors
(
Poster
)
>
|
Marco Knipfer · Sergei Gleyzer · Stefan Meier · Jonas Heimerl · Peter Hommelhoff 🔗 |
|
-
|
Tensor networks for active inference with discrete observation spaces
(
Poster
)
>
|
Samuel T. Wauthier · Bram Vanhecke · Tim Verbelen · Bart Dhoedt 🔗 |
|
-
|
Employing CycleGANs to Generate Realistic STEM Images for Machine Learning
(
Poster
)
>
|
Abid Khan · Chia-Hao Lee · Pinshane Y. Huang · Bryan Clark 🔗 |
|
-
|
HubbardNet: Efficient Predictions of the Bose-Hubbard Model Spectrum with Deep Neural Networks
(
Poster
)
>
|
Ziyan Zhu · Marios Mattheakis · Weiwei Pan · Efthimios Kaxiras 🔗 |
|
-
|
Strong-Lensing Source Reconstruction with Denoising Diffusion Restoration Models
(
Poster
)
>
|
Konstantin Karchev · Noemi Anau Montel · Adam Coogan · Christoph Weniger 🔗 |
|
-
|
Score-based Seismic Inverse Problems
(
Poster
)
>
|
Sriram Ravula · Dimitri Voytan · Elad Liebman · Ram Tuvi · Yash Gandhi · Hamza Ghani · Alex Ardel · Mrinal Sen · Alex Dimakis 🔗 |
|
-
|
Deep-pretrained-FWI: combining supervised learning with physics-informed neural network
(
Poster
)
>
|
ANA PAULA MULLER · Clecio Roque Bom · Jessé Carvalho Costa · Elisângela Lopes Faria · Marcelo Portes de Albuquerque · Marcio Portes de Albuquerque 🔗 |
|
-
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Differentiable composition for model discovery
(
Poster
)
>
|
Omer Rochman Sharabi · Gilles Louppe 🔗 |
|
-
|
Improving Generalization with Physical Equations
(
Poster
)
>
|
Antoine Wehenkel · Jens Behrmann · Hsiang Hsu · Guillermo Sapiro · Gilles Louppe · Joern-Henrik Jacobsen 🔗 |
|
-
|
Neural Fields for Fast and Scalable Interpolation of Geophysical Ocean Variables
(
Poster
)
>
|
Juan Emmanuel Johnson · Redouane Lguensat · ronan fablet · Emmanuel Cosme · Julien Le Sommer 🔗 |
|
-
|
Interpretable Encoding of Galaxy Spectra
(
Poster
)
>
|
Yan Liang · Peter Melchior · Sicong Lu 🔗 |
|
-
|
Neural Network Prior Mean for Particle Accelerator Injector Tuning
(
Poster
)
>
|
Connie Xu · Ryan Roussel · Auralee Edelen 🔗 |
|
-
|
Applications of Differentiable Physics Simulations in Particle Accelerator Modeling
(
Poster
)
>
|
Ryan Roussel · Auralee Edelen 🔗 |
|
-
|
A robust estimator of mutual information for deep learning interpretability
(
Poster
)
>
|
Davide Piras · Hiranya Peiris · Andrew Pontzen · Luisa Lucie-Smith · Brian Nord · Ningyuan (Lillian) Guo 🔗 |
|
-
|
Finding NEEMo: Geometric Fitting using Neural Estimation of the Energy Mover’s Distance
(
Poster
)
>
|
Ouail Kitouni · Mike Williams · Niklas S Nolte 🔗 |
|
-
|
DIGS: Deep Inference of Galaxy Spectra with Neural Posterior Estimation
(
Poster
)
>
|
Gourav Khullar · Brian Nord · Aleksandra Ciprijanovic · Jason Poh · Fei Xu · Ashwin Samudre 🔗 |
|
-
|
Strong Lensing Parameter Estimation on Ground-Based Imaging Data Using Simulation-Based Inference
(
Poster
)
>
|
Jason Poh · Ashwin Samudre · Aleksandra Ciprijanovic · Brian Nord · Joshua Frieman · Gourav Khullar 🔗 |
|
-
|
Closing the resolution gap in Lyman alpha simulations with deep learning
(
Poster
)
>
|
Cooper Jacobus · Peter Harrington · Zarija Lukić 🔗 |
|
-
|
Physics-Informed Convolutional Neural Networks for Corruption Removal on Dynamical Systems
(
Poster
)
>
|
Daniel Kelshaw · Luca Magri 🔗 |
|
-
|
Do graph neural networks learn jet substructure?
(
Poster
)
>
|
Farouk Mokhtar · Raghav Kansal · Javier Duarte 🔗 |
|
-
|
Thermophysical Change Detection on the Moon with the Lunar Reconnaissance Orbiter Diviner sensor
(
Poster
)
>
|
Jose Delgado-Centeno · Silvia Bucci · Ziyi Liang · Ben Gaffinet · Valentin T. Bickel · Ben Moseley · Miguel Olivares 🔗 |
|
-
|
Source Identification and Field Reconstruction of Advection-Diffusion Process from Sparse Sensor Measurements
(
Poster
)
>
|
Arka Daw · Kyongmin Yeo · Anuj Karpatne · 🔗 |
|
-
|
Geometry-aware Autoregressive Models for Calorimeter Shower Simulations
(
Poster
)
>
|
Junze Liu · Aishik Ghosh · Dylan Smith · Pierre Baldi · Daniel Whiteson 🔗 |
|
-
|
Characterizing information loss in a chaotic double pendulum with the Information Bottleneck
(
Poster
)
>
|
Kieran Murphy · Danielle S Bassett 🔗 |
|
-
|
Detecting structured signals in radio telescope data using RKHS
(
Poster
)
>
|
Russell Tsuchida · Suk Yee Yong 🔗 |
|
-
|
Statistical Inference for Coadded Astronomical Images
(
Poster
)
>
|
Mallory Wang · Ismael Mendoza · Jeffrey Regier · Camille Avestruz · Cheng Wang 🔗 |
|
-
|
Domain Adaptation for Simulation-Based Dark Matter Searches with Strong Gravitational Lensing
(
Poster
)
>
|
Pranath Reddy Kumbam · Sergei Gleyzer · Michael Toomey · Marcos Tidball 🔗 |
|
-
|
A hybrid Reduced Basis and Machine-Learning algorithm for building Surrogate Models: a first application to electromagnetism
(
Poster
)
>
|
Alejandro Ribes · Ruben Persicot · Lucas Meyer · Jean-Pierre Ducreux 🔗 |
|
-
|
Data-driven discovery of non-Newtonian astronomy via learning non-Euclidean Hamiltonian
(
Poster
)
>
|
Oswin So · Gongjie Li · Evangelos Theodorou · Molei Tao 🔗 |
|
-
|
Deconvolving Detector Effects for Distribution Moments
(
Poster
)
>
|
Krish Desai · Benjamin Nachman · Jesse Thaler 🔗 |
|
-
|
Multi-scale Digital Twin: Developing a fast and physics-infused surrogate model for groundwater contamination with uncertain climate models
(
Poster
)
>
|
Lijing Wang · Takuya Kurihana · Aurelien Meray · Ilijana Mastilovic · Satyarth Praveen · Zexuan Xu · Milad Memarzadeh · Alexander Lavin · Haruko Wainwright 🔗 |
|
-
|
Topological Jet Tagging
(
Poster
)
>
|
Dawson Thomas · Sarah Demers · Smita Krishnaswamy · Bastian Rieck 🔗 |
|
-
|
Physics-Driven Convolutional Autoencoder Approach for CFD Data Compressions
(
Poster
)
>
|
Alberto Olmo · Ahmed Zamzam · Andrew Glaws · Ryan King 🔗 |
|
-
|
Recovering Galaxy Cluster Convergence from Lensed CMB with Generative Adversarial Networks
(
Poster
)
>
|
Liam Parker · Dongwon Han · Shirley Ho · Pablo Lemos 🔗 |
|
-
|
DiffDock: Diffusion Steps, Twists, and Turns for Molecular Docking
(
Poster
)
>
|
Gabriele Corso · Hannes Stärk · Bowen Jing · Regina Barzilay · Tommi Jaakkola 🔗 |
|
-
|
Normalizing Flows for Fragmentation and Hadronization
(
Poster
)
>
|
Ahmed Youssef · Philip Ilten · Tony Menzo · Jure Zupan · Manuel Szewc · Stephen Mrenna · Michael K. Wilkinson 🔗 |
|
-
|
Astronomical Image Coaddition with Bundle-Adjusting Radiance Fields
(
Poster
)
>
|
Harlan Hutton · Harshitha Palegar · Shirley Ho · Miles Cranmer · Peter Melchior · Jenna Eubank 🔗 |
|
-
|
Differentiable Physics-based Greenhouse Simulation
(
Poster
)
>
|
Nhat M. Nguyen · Hieu Tran · Minh Duong · Hanh Bui · Kenneth Tran 🔗 |
|
-
|
Plausible Adversarial Attacks on Direct Parameter Inference Models in Astrophysics
(
Poster
)
>
|
Benjamin Horowitz · Peter Melchior 🔗 |
|
-
|
GAN-Flow: A dimension-reduced variational framework for physics-based inverse problems
(
Poster
)
>
|
Agnimitra Dasgupta · Dhruv Patel · Deep Ray · Erik Johnson · Assad Oberai 🔗 |
|
-
|
Control and Calibration of GlueX Central Drift Chamber Using Gaussian Process Regression
(
Poster
)
>
|
Diana McSpadden · Torri Jeske · Naomi Jarvis · David Lawrence · Thomas Britton · nikhil kalra 🔗 |
|
-
|
Emulating cosmological growth functions with B-Splines
(
Poster
)
>
|
Ngai Pok Kwan · Chirag Modi · Yin Li · Shirley Ho 🔗 |
|
-
|
ClimFormer - a Spherical Transformer model for long-term climate projections
(
Poster
)
>
|
Salva Rühling Cachay · Peetak Mitra · Sookyung Kim · Subhashis Hazarika · Haruki Hirasawa · Dipti Hingmire · Hansi Singh · Kalai Ramea 🔗 |
|
-
|
Computing the Bayes-optimal classifier and exact maximum likelihood estimator with a semi-realistic generative model for jet physics
(
Poster
)
>
|
Kyle Cranmer · Matthew Drnevich · Lauren Greenspan · Sebastian Macaluso · Duccio Pappadopulo 🔗 |
|
-
|
The Senseiver: attention-based global field reconstruction from sparse observations
(
Poster
)
>
|
Javier E. Santos · Zachary Fox · Arvind Mohan · Hari Viswanathan · NIcholas Lubbers 🔗 |
|
-
|
SE(3)-equivariant self-attention via invariant features
(
Poster
)
>
|
Nan Chen · Soledad Villar 🔗 |
|
-
|
Skip Connections for High Precision Regressors
(
Poster
)
>
|
Ayan Paul · Fady Bishara · Jennifer Dy 🔗 |
|
-
|
Likelihood-Free Frequentist Inference for Calorimetric Muon Energy Measurement in High-Energy Physics
(
Poster
)
>
|
Luca Masserano · Ann Lee · Rafael Izbicki · Mikael Kuusela · tommaso dorigo 🔗 |
|
-
|
Uncertainty Aware Deep Learning for Particle Accelerators
(
Poster
)
>
|
Kishansingh Rajput · Malachi Schram · Karthik Somayaji NS 🔗 |
|
-
|
Graphical Models are All You Need: Per-interaction reconstruction uncertainties in a dark matter detection experiment
(
Poster
)
>
|
Christina Peters · Aaron Higuera · Shixiao Liang · Waheed Bajwa · Christopher Tunnell 🔗 |
|
-
|
PELICAN: Permutation Equivariant and Lorentz Invariant or Covariant Aggregator Network for Particle Physics
(
Poster
)
>
|
Jan Offermann · Alexander Bogatskiy · Timothy Hoffman · David W Miller 🔗 |
|
-
|
FO-PINNs: A First-Order formulation for Physics~Informed Neural Networks
(
Poster
)
>
|
Rini Jasmine Gladstone · Mohammad Amin Nabian · Hadi Meidani 🔗 |
|
-
|
Learning the nonlinear manifold of extreme aerodynamics
(
Poster
)
>
|
Kai Fukami · Kunihiko Taira 🔗 |
|
-
|
Geometric NeuralPDE (GNPnet) Models for Learning Dynamics
(
Poster
)
>
|
Oluwadamilola Fasina · Smita Krishnaswamy · Aditi Krishnapriyan 🔗 |
|
-
|
Can denoising diffusion probabilistic models generate realistic astrophysical fields?
(
Poster
)
>
|
Nayantara Mudur · Douglas P. Finkbeiner 🔗 |
|
-
|
PIPS: Path Integral Stochastic Optimal Control for Path Sampling in Molecular Dynamics
(
Poster
)
>
|
Lars Holdijk · Yuanqi Du · Ferry Hooft · Priyank Jaini · Berend Ensing · Max Welling 🔗 |
|
-
|
Predicting Full-Field Turbulent Flows Using Fourier Neural Operator
(
Poster
)
>
|
Peter Renn · Sahin Lale · Cong Wang · Zongyi Li · Anima Anandkumar · Morteza Gharib 🔗 |
|
-
|
A Self-Supervised Approach to Reconstruction in Sparse X-Ray Computed Tomography
(
Poster
)
>
|
Rey Mendoza · Minh Nguyen · Judith Weng Zhu · Talita Perciano · Vincent Dumont · Juliane Mueller · Vidya Ganapati 🔗 |
|
-
|
Energy based models for tomography of quantum spin-lattice systems
(
Poster
)
>
|
Abhijith Jayakumar · Marc Vuffray · Andrey Lokhov 🔗 |
|
-
|
Elements of effective machine learning datasets in astronomy
(
Poster
)
>
|
Bernie Boscoe · Tuan Do 🔗 |
|
-
|
Towards a non-Gaussian Generative Model of large-scale Reionization Maps
(
Poster
)
>
|
Yu-Heng Lin · Sultan Hassan · Bruno Régaldo-Saint Blancard · Michael Eickenberg · Chirag Modi 🔗 |
|
-
|
Adversarial Noise Injection for Learned Turbulence Simulations
(
Poster
)
>
|
Jingtong Su · Julia Kempe · Drummond Fielding · Nikolaos Tsilivis · Miles Cranmer · Shirley Ho 🔗 |
|
-
|
Shining light on data
(
Poster
)
>
|
Akshat Kumar · Mohan Sarovar 🔗 |
|
-
|
A Novel Automatic Mixed Precision Approach For Physics Informed Training
(
Poster
)
>
|
Jinze Xue · Akshay Subramaniam · Mark Hoemmen 🔗 |
|
-
|
Atmospheric retrievals of exoplanets using learned parameterizations of pressure-temperature profiles
(
Poster
)
>
|
Timothy Gebhard · Daniel Angerhausen · Björn Konrad · Eleonora Alei · Sascha Quanz · Bernhard Schölkopf 🔗 |
|
-
|
Probabilistic Mixture Modeling For End-Member Extraction in Hyperspectral Data
(
Poster
)
>
|
Oliver Hoidn · Aashwin Mishra · Apurva Mehta 🔗 |
|
-
|
Posterior samples of source galaxies in strong gravitational lenses with score-based priors
(
Poster
)
>
|
Alexandre Adam · Adam Coogan · Nikolay Malkin · Ronan Legin · Laurence Perreault-Levasseur · Yashar Hezaveh · Yoshua Bengio 🔗 |
|
-
|
Particle-level Compression for New Physics Searches
(
Poster
)
>
|
Yifeng Huang · Jack Collins · Benjamin Nachman · Simon Knapen · Daniel Whiteson 🔗 |
|
-
|
CaloMan: Fast generation of calorimeter showers with density estimation on learned manifolds
(
Poster
)
>
|
Jesse Cresswell · Brendan Ross · Gabriel Loaiza-Ganem · Humberto Reyes-Gonzalez · Marco Letizia · Anthony Caterini 🔗 |
|
-
|
De-noising non-Gaussian fields in cosmology with normalizing flows
(
Poster
)
>
|
Adam Rouhiainen · Moritz Münchmeyer 🔗 |
|
-
|
Learning Integrable Dynamics with Action-Angle Networks
(
Poster
)
>
|
Ameya Daigavane · Arthur Kosmala · Miles Cranmer · Tess Smidt · Shirley Ho 🔗 |
|
-
|
Physics-informed Bayesian Optimization of an Electron Microscope
(
Poster
)
>
|
Desheng Ma 🔗 |
|
-
|
Why are deep learning-based models of geophysical turbulence long-term unstable?
(
Poster
)
>
|
Ashesh Chattopadhyay · Pedram Hassanzadeh 🔗 |
|
-
|
Graph Structure from Point Clouds: Geometric Attention is All You Need
(
Poster
)
>
|
Daniel Murnane 🔗 |
|
-
|
One-Class Dense Networks for Anomaly Detection
(
Poster
)
>
|
Norman Karr · Benjamin Nachman · David Shih 🔗 |
|
-
|
Self-supervised detection of atmospheric phenomena from remotely sensed synthetic aperture radar imagery
(
Poster
)
>
|
Yannik Glaser · Peter Sadowski · Justin Stopa 🔗 |
|
-
|
Emulating cosmological multifields with generative adversarial networks
(
Poster
)
>
|
Sambatra Andrianomena · Sultan Hassan · Francisco Villaescusa-Navarro 🔗 |
|
-
|
Monte Carlo Techniques for Addressing Large Errors and Missing Data in Simulation-based Inference
(
Poster
)
>
|
Bingjie Wang · Joel Leja · Victoria Villar · Joshua Speagle 🔗 |
|
-
|
Towards Creating Benchmark Datasets of Universal Neural Network Potential for Material Discovery
(
Poster
)
>
|
So Takamoto · Chikashi Shinagawa · Nontawat Charoenphakdee 🔗 |
|
-
|
Physics-informed neural networks for modeling rate- and temperature-dependent plasticity
(
Poster
)
>
|
Rajat Arora · Pratik Kakkar · Amit Chakraborty · Biswadip Dey 🔗 |
|
-
|
A Trust Crisis In Simulation-Based Inference? Your Posterior Approximations Can Be Unfaithful
(
Poster
)
>
|
Joeri Hermans · Arnaud Delaunoy · François Rozet · Antoine Wehenkel · Volodimir Begy · Gilles Louppe 🔗 |
|
-
|
Learning-based solutions to nonlinear hyperbolic PDEs: Empirical insights on generalization errors
(
Poster
)
>
|
Bilal Thonnam Thodi · Sai Venkata Ramana Ambadipudi · Saif Eddin Jabari 🔗 |
|
-
|
Modeling halo and central galaxy orientations on the SO(3) manifold with score-based generative models
(
Poster
)
>
|
Yesukhei Jagvaral · Francois Lanusse · Rachel Mandelbaum 🔗 |
|
-
|
Improved Training of Physics-informed Neural Networks using Energy-Based priors: A Study on Electrical Impedance Tomography
(
Poster
)
>
|
Akarsh Pokkunuru · Pedram Rooshenas · Thilo Strauss · Anuj Abhishek · Taufiquar Khan 🔗 |
|
-
|
Geometric path augmentation for inference of sparsely observed stochastic nonlinear systems
(
Poster
)
>
|
Dimitra Maoutsa 🔗 |
|
-
|
How good is the Standard Model? Machine learning multivariate Goodness of Fit tests
(
Poster
)
>
|
Gaia Grosso · Marco Letizia · Andrea Wulzer · Maurizio Pierini 🔗 |
|
-
|
A probabilistic deep learning model to distinguish cusps and cores in dwarf galaxies
(
Poster
)
>
|
Julen Expósito Márquez · Marc Huertas-Company · Arianna Di Cintio · Chris Brook · Andrea Macciò · Rob Grant · Elena Arjona 🔗 |
|
-
|
Super-resolving Dark Matter Halos using Generative Deep Learning
(
Poster
)
>
|
David Schaurecker 🔗 |
|
-
|
Using Shadows to Learn Ground State Properties of Quantum Hamiltonians
(
Poster
)
>
|
Viet T. Tran · Laura Lewis · Johannes Kofler · Hsin-Yuan Huang · Richard Kueng · Sepp Hochreiter · Sebastian Lehner 🔗 |
|
-
|
Set-Conditional Set Generation for Particle Physics
(
Poster
)
>
|
Sanmay Ganguly · Lukas Heinrich · Nilotpal Kakati · Nathalie Soybelman 🔗 |
|
-
|
Score Matching via Differentiable Physics
(
Poster
)
>
|
Benjamin Holzschuh · Simona Vegetti · Nils Thuerey 🔗 |
|
-
|
Adaptive Selection of Atomic Fingerprints for High-Dimensional Neural Network Potentials
(
Poster
)
>
|
Johannes Sandberg · Emilie Devijver · Noel Jakse · Thomas Voigtmann 🔗 |
|
-
|
HyperFNO: Improving the Generalization Behavior of Fourier Neural Operators
(
Poster
)
>
|
Francesco Alesiani · Makoto Takamoto · Mathias Niepert 🔗 |
|
-
|
Normalizing Flows for Hierarchical Bayesian Analysis: A Gravitational Wave Population Study
(
Poster
)
>
|
David Ruhe · Kaze Wong · Miles Cranmer · Patrick Forré 🔗 |
|
-
|
Fast kinematics modeling for conjunction with lens image modeling
(
Poster
)
>
|
Matthew Gomer · Luca Biggio · Sebastian Ertl · Han Wang · Aymeric Galan · Lyne Van de Vyvere · Dominique Sluse · Georgios Vernardos · Sherry Suyu 🔗 |
|
-
|
Multi-Fidelity Transfer Learning for accurate database PDE approximation
(
Poster
)
>
|
Wenzhuo LIU · Mouadh Yagoubi · Marc Schoenauer · David Danan 🔗 |
|
-
|
Learning Electron Bunch Distribution along a FEL Beamline by Normalising Flows
(
Poster
)
>
|
Anna Willmann · Jurjen Pieter Couperus Cabadağ · Yen-Yu Chang · Richard Pausch · Amin Ghaith · Alexander Debus · Arie Irman · Michael Bussmann · Ulrich Schramm · Nico Hoffmann 🔗 |
|
-
|
Continual learning autoencoder training for a particle-in-cell simulation via streaming
(
Poster
)
>
|
Patrick Stiller · Varun Makdani · Franz Poeschel · Richard Pausch · Alexander Debus · Michael Bussmann · Nico Hoffmann 🔗 |
|
-
|
On Using Deep Learning Proxies as Forward Models in Optimization Problems
(
Poster
)
>
|
Fatima Albreiki · Nidhal Belayouni · Deepak Gupta 🔗 |
|
-
|
HGPflow: Particle reconstruction as hyperedge prediction
(
Poster
)
>
|
Etienne Dreyer · Nilotpal Kakati · Francesco Armando Di Bello 🔗 |
|
-
|
Anomaly Detection with Multiple Reference Datasets in High Energy Physics
(
Poster
)
>
|
Mayee Chen · Benjamin Nachman · Frederic Sala 🔗 |
|
-
|
Do Better QM9 Models Extrapolate as Better Quantum Chemical Property Predictors?
(
Poster
)
>
|
YUCHENG ZHANG · Nontawat Charoenphakdee · So Takamoto 🔗 |
|
-
|
Diversity Balancing Generative Adversarial Networks for fast simulation of the Zero Degree Calorimeter in the ALICE experiment at CERN
(
Poster
)
>
|
Jan Dubiński · Kamil Deja · Sandro Wenzel · Przemysław Rokita · Tomasz Trzcinski 🔗 |
|
-
|
Identifying Hamiltonian Manifold in Neural Networks
(
Poster
)
>
|
Yeongwoo Song · Hawoong Jeong 🔗 |
|
-
|
Physics-Informed Neural Networks as Solvers for the Time-Dependent Schrödinger Equation
(
Poster
)
>
|
Karan Shah · Patrick Stiller · Nico Hoffmann · Attila Cangi 🔗 |
|
-
|
Time-aware Bayesian optimization for adaptive particle accelerator tuning
(
Poster
)
>
|
Nikita Kuklev · Yine Sun · Hairong Shang · Michael Borland · Gregory Fystro 🔗 |
|
-
|
Inferring molecular complexity from mass spectrometry data using machine learning
(
Poster
)
>
|
Timothy Gebhard · Aaron C. Bell · Jian Gong · Jaden J. A. Hastings · George Fricke · Nathalie Cabrol · Scott Sandford · Michael Phillips · Kimberley Warren-Rhodes · Atilim Gunes Baydin 🔗 |
|
-
|
A physics-informed search for metric solutions to Ricci flow, their embeddings, and visualisation
(
Poster
)
>
|
Aarjav Jain · Challenger Mishra · Pietro Lió 🔗 |
|
-
|
Detection is truncation: studying source populations with truncated marginal neural ratio estimation
(
Poster
)
>
|
Noemi Anau Montel · Christoph Weniger 🔗 |
|
-
|
Galaxy Morphological Classification with Deformable Attention Transformer
(
Poster
)
>
|
SEOKUN KANG · Min-Su Shin · Taehwan Kim 🔗 |
|
-
|
Towards solving model bias in cosmic shear forward modeling
(
Poster
)
>
|
Benjamin Remy · Francois Lanusse · Jean-Luc Starck 🔗 |
|
-
|
Physical Data Models in Machine Learning Imaging Pipelines
(
Poster
)
>
|
Marco Aversa · Luis Oala · Christoph Clausen · Roderick Murray-Smith · Bruno Sanguinetti 🔗 |
|
-
|
Amortized Bayesian Inference of GISAXS Data with Normalizing Flows
(
Poster
)
>
|
Maksim Zhdanov · Lisa Randolph · Thomas Kluge · Motoaki Nakatsutsumi · Christian Gutt · Marina Ganeva · Nico Hoffmann 🔗 |
|
-
|
Insight into cloud processes from unsupervised classification with a rotation-invariant autoencoder
(
Poster
)
>
|
Takuya Kurihana · James Franke · Ian Foster · Ziwei Wang · Elisabeth Moyer 🔗 |
|
-
|
Addressing out-of-distribution data for flow-based gravitational wave inference
(
Poster
)
>
|
Maximilian Dax · Stephen Green · Jonas Wildberger · Jonathan Gair · Michael Puerrer · Jakob Macke · Alessandra Buonanno · Bernhard Schölkopf 🔗 |
|
-
|
A fast and flexible machine learning approach to data quality monitoring
(
Poster
)
>
|
Marco Letizia · Gaia Grosso · Andrea Wulzer · Marco Zanetti · Jacopo Pazzini · Marco Rando · Nicolò Lai 🔗 |
|
-
|
Cosmology from Galaxy Redshift Surveys with PointNet
(
Poster
)
>
|
Sotiris Anagnostidis · Arne Thomsen · Alexandre Refregier · Tomasz Kacprzak · Luca Biggio · Thomas Hofmann · Tilman Tröster 🔗 |
|
-
|
Finding active galactic nuclei through Fink
(
Poster
)
>
|
Etienne Russeil · Emille Ishida · Julien Peloton · Anais Möller · Roman Le Montagner 🔗 |