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Sat Dec 03 05:50 AM -- 06:00 AM (PST) None
Opening remarks
In Machine Learning and the Physical Sciences
Sat Dec 03 06:00 AM -- 06:30 AM (PST) None
Invited talk: David Pfau, "Deep Learning and Ab-Initio Quantum Chemistry and Materials"
In Machine Learning and the Physical Sciences
David Pfau · Siddharth Mishra-Sharma
Sat Dec 03 06:30 AM -- 06:45 AM (PST) None
Contributed talk: Kieran Murphy, "Characterizing information loss in a chaotic double pendulum with the Information Bottleneck"
In Machine Learning and the Physical Sciences
Kieran Murphy · Siddharth Mishra-Sharma
Sat Dec 03 06:45 AM -- 07:15 AM (PST) None
Invited talk: Hiranya Peiris, "Prospects for understanding the physics of the Universe"
In Machine Learning and the Physical Sciences
Hiranya Peiris · Siddharth Mishra-Sharma
Sat Dec 03 07:15 AM -- 07:30 AM (PST) None
Contributed talk: Marco Aversa, "Physical Data Models in Machine Learning Imaging Pipelines"
In Machine Learning and the Physical Sciences
Marco Aversa · Siddharth Mishra-Sharma
Sat Dec 03 07:30 AM -- 08:00 AM (PST) None
Invited Talk: Giorgio Parisi
In Machine Learning and the Physical Sciences
Sat Dec 03 08:00 AM -- 09:00 AM (PST) None
Poster session 1 and break
In Machine Learning and the Physical Sciences
Sat Dec 03 09:00 AM -- 10:00 AM (PST) None
Panel: Kathleen Creel, Mario Krenn, and Emily Sullivan, "Philosophy of Science in the AI Era"
In Machine Learning and the Physical Sciences
Sat Dec 03 10:00 AM -- 11:15 AM (PST) None
Lunch
In Machine Learning and the Physical Sciences
Sat Dec 03 11:15 AM -- 11:45 AM (PST) None
Invited talk: E. Doğuş Çubuk, "Scaling up material discovery via deep learning"
In Machine Learning and the Physical Sciences
Ekin Dogus Cubuk · Siddharth Mishra-Sharma
Sat Dec 03 11:45 AM -- 12:15 PM (PST) None
Invited talk: Vinicius Mikuni, "Collider Physics Innovations Powered by Machine Learning"
In Machine Learning and the Physical Sciences
Vinicius Mikuni · Siddharth Mishra-Sharma
Sat Dec 03 12:15 PM -- 12:30 PM (PST) None
Contributed talk: Aurélien Dersy, "Simplifying Polylogarithms with Machine Learning"
In Machine Learning and the Physical Sciences
Aurelien Dersy · Siddharth Mishra-Sharma
Sat Dec 03 12:30 PM -- 01:00 PM (PST) None
Invited talk: Federico Felici, "Magnetic control of tokamak plasmas through Deep Reinforcement Learning"
In Machine Learning and the Physical Sciences
Federico Felici · Siddharth Mishra-Sharma
Sat Dec 03 01:00 PM -- 01:15 PM (PST) None
Contributed talk: Alexandre Adam, "Posterior samples of source galaxies in strong gravitational lenses with score-based priors"
In Machine Learning and the Physical Sciences
Alexandre Adam · Siddharth Mishra-Sharma
Sat Dec 03 01:15 PM -- 01:30 PM (PST) None
Break
In Machine Learning and the Physical Sciences
Sat Dec 03 01:30 PM -- 02:00 PM (PST) None
Invited talk: Catherine Nakalembe and Hannah Kerner
In Machine Learning and the Physical Sciences
Catherine Nakalembe · Hannah Kerner · Siddharth Mishra-Sharma
Sat Dec 03 02:00 PM -- 02:05 PM (PST) None
Closing remarks
In Machine Learning and the Physical Sciences
Sat Dec 03 02:05 PM -- 03:00 PM (PST) None
Poster session 2
In Machine Learning and the Physical Sciences
None
Molecular Fingerprints for Robust and Efficient ML-Driven Molecular Generation
In Machine Learning and the Physical Sciences
Ruslan Tazhigulov · Joshua Schiller · Jacob Oppenheim · Max Winston
[ Poster] 
None
Machine Learning for Chemical Reactions \\A Dance of Datasets and Models
In Machine Learning and the Physical Sciences
Mathias Schreiner · Arghya Bhowmik · Tejs Vegge · Jonas Busk · Peter Bjørn Jørgensen · Ole Winther
[ Poster] 
None
A New Task: Deriving Semantic Class Targets for the Physical Sciences
In Machine Learning and the Physical Sciences
Micah Bowles
[ Poster] 
None
Training physical networks like neural networks: deep physical neural networks
In Machine Learning and the Physical Sciences
Logan Wright · Tatsuhiro Onodera · Martin M Stein · Tianyu Wang · Darren Schachter · Zoey Hu · Peter McMahon
[ Poster] 
None
A Curriculum-Training-Based Strategy for Distributing Collocation Points during Physics-Informed Neural Network Training
In Machine Learning and the Physical Sciences
Marcus Münzer · Christopher Bard
[ Slides]  [ Poster] 
None
Learning latent variable evolution for the functional renormalization group
In Machine Learning and the Physical Sciences
Matija Medvidović · Alessandro Toschi · Giorgio Sangiovanni · Cesare Franchini · Andy Millis · Anirvan Sengupta · Domenico Di Sante
[ Poster] 
None
Deformations of Boltzmann Distributions
In Machine Learning and the Physical Sciences
Bálint Máté · François Fleuret
[ Poster] 
None
Generating astronomical spectra from photometry with conditional diffusion models
In Machine Learning and the Physical Sciences
Lars Doorenbos · Stefano Cavuoti · Giuseppe Longo · Massimo Brescia · Raphael Sznitman · Pablo Márquez Neila
[ Poster] 
None
Identifying AGN host galaxies with convolutional neural networks
In Machine Learning and the Physical Sciences
Ziting Guo · John Wu · Chelsea Sharon
[ Poster] 
None
Efficiently Moving Instead of Reweighting Collider Events with Machine Learning
In Machine Learning and the Physical Sciences
Radha Mastandrea · Benjamin Nachman
[ Poster] 
None
D-optimal neural exploration of nonlinear physical systems
In Machine Learning and the Physical Sciences
Matthieu Blanke · marc lelarge
[ Poster] 
None
NLP Inspired Training Mechanics For Modeling Transient Dynamics
In Machine Learning and the Physical Sciences
Lalit Ghule · Rishikesh Ranade · Jay Pathak
[ Poster] 
None
Reducing Down(stream)time: Pretraining Molecular GNNs using Heterogeneous AI Accelerators
In Machine Learning and the Physical Sciences
Jenna A Bilbrey · Kristina Herman · Henry Sprueill · Sotiris Xantheas · Payel Das · Manuel Lopez Roldan · Mike Kraus · Hatem Helal · Sutanay Choudhury
[ Poster] 
None
Qubit seriation: Undoing data shuffling using spectral ordering
In Machine Learning and the Physical Sciences
Atithi Acharya · Manuel Rudolph · Jing Chen · Jacob Miller · Alejandro Perdemo-Ortiz
[ Poster] 
None
Clustering Behaviour of Physics-Informed Neural Networks: Inverse Modeling of An Idealized Ice Shelf
In Machine Learning and the Physical Sciences
Yunona Iwasaki · Ching-Yao Lai
[ Poster] 
None
Renormalization in the neural network-quantum field theory correspondence
In Machine Learning and the Physical Sciences
Harold Erbin · Vincent Lahoche · Dine Ousmane Samary
[ Poster] 
None
Combinational-convolution for flow-based sampling algorithm
In Machine Learning and the Physical Sciences
Akio Tomiya
[ Poster] 
None
Stabilization and Acceleration of CFD Simulation by Controlling Relaxation Factor Based on Residues: An SNN Based Approach
In Machine Learning and the Physical Sciences
Sounak Dey · Dighanchal Banerjee · Mithilesh Maurya · Dilshad Ahmad
[ Poster] 
None
Contrasting random and learned features in deep Bayesian linear regression
In Machine Learning and the Physical Sciences
Jacob Zavatone-Veth · William Tong · Cengiz Pehlevan
[ Poster] 
None
DS-GPS : A Deep Statistical Graph Poisson Solver (for faster CFD simulations)
In Machine Learning and the Physical Sciences
Matthieu Nastorg
[ Poster] 
None
Dynamical Mean Field Theory of Kernel Evolution in Wide Neural Networks
In Machine Learning and the Physical Sciences
Blake Bordelon · Cengiz Pehlevan
[ Poster] 
None
Semi-Supervised Domain Adaptation for Cross-Survey Galaxy Morphology Classification and Anomaly Detection
In Machine Learning and the Physical Sciences
Aleksandra Ciprijanovic · Ashia Lewis · Kevin Pedro · Sandeep Madireddy · Brian Nord · Gabriel Nathan Perdue · Stefan Wild
[ Poster] 
None
A Neural Network Subgrid Model of the Early Stages of Planet Formation
In Machine Learning and the Physical Sciences
Thomas Pfeil · Miles Cranmer · Shirley Ho · Philip Armitage · Tilman Birnstiel · Hubert Klahr
[ Poster] 
None
Validation Diagnostics for SBI algorithms based on Normalizing Flows
In Machine Learning and the Physical Sciences
Julia Linhart · Alexandre Gramfort · Pedro Rodrigues
[ Poster] 
None
One Network to Approximate Them All: Amortized Variational Inference of Ising Ground States
In Machine Learning and the Physical Sciences
Sebastian Sanokowski · Wilhelm Berghammer · Johannes Kofler · Sepp Hochreiter · Sebastian Lehner
[ Poster] 
None
Hybrid integration of the gravitational N-body problem with Artificial Neural Networks
In Machine Learning and the Physical Sciences
Veronica Saz Ulibarrena · Simon Portegies Zwart · Elena Sellentin · Barry Koren · Philipp Horn · Maxwell X. Cai
[ Poster] 
None
Physics-Informed CNNs for Super-Resolution of Sparse Observations on Dynamical Systems
In Machine Learning and the Physical Sciences
Daniel Kelshaw · Georgios Rigas · Luca Magri
[ Poster] 
None
Deep Learning-Based Spatiotemporal Multi-Event Reconstruction for Delay-Line Detectors
In Machine Learning and the Physical Sciences
Marco Knipfer · Sergei Gleyzer · Stefan Meier · Jonas Heimerl · Peter Hommelhoff
[ Poster] 
None
HubbardNet: Efficient Predictions of the Bose-Hubbard Model Spectrum with Deep Neural Networks
In Machine Learning and the Physical Sciences
Ziyan Zhu · Marios Mattheakis · Weiwei Pan · Efthimios Kaxiras
[ Poster] 
None
Neural Fields for Fast and Scalable Interpolation of Geophysical Ocean Variables
In Machine Learning and the Physical Sciences
Juan Emmanuel Johnson · Redouane Lguensat · ronan fablet · Emmanuel Cosme · Julien Le Sommer
[ Poster] 
None
Interpretable Encoding of Galaxy Spectra
In Machine Learning and the Physical Sciences
Yan Liang · Peter Melchior · Sicong Lu
[ Poster] 
None
Neural Network Prior Mean for Particle Accelerator Injector Tuning
In Machine Learning and the Physical Sciences
Connie Xu · Ryan Roussel · Auralee Edelen
[ Poster] 
None
A robust estimator of mutual information for deep learning interpretability
In Machine Learning and the Physical Sciences
Davide Piras · Hiranya Peiris · Andrew Pontzen · Luisa Lucie-Smith · Brian Nord · Ningyuan (Lillian) Guo
[ Poster] 
None
Finding NEEMo: Geometric Fitting using Neural Estimation of the Energy Mover’s Distance
In Machine Learning and the Physical Sciences
Ouail Kitouni · Mike Williams · Niklas S Nolte
[ Poster] 
None
Physics-Informed Convolutional Neural Networks for Corruption Removal on Dynamical Systems
In Machine Learning and the Physical Sciences
Daniel Kelshaw · Luca Magri
[ Poster] 
None
Do graph neural networks learn jet substructure?
In Machine Learning and the Physical Sciences
Farouk Mokhtar · Raghav Kansal · Javier Duarte
[ Poster] 
None
Thermophysical Change Detection on the Moon with the Lunar Reconnaissance Orbiter Diviner sensor
In Machine Learning and the Physical Sciences
Jose Delgado-Centeno · Silvia Bucci · Ziyi Liang · Ben Gaffinet · Valentin T. Bickel · Ben Moseley · Miguel Olivares
[ Poster] 
None
Characterizing information loss in a chaotic double pendulum with the Information Bottleneck
In Machine Learning and the Physical Sciences
Kieran Murphy · Danielle S Bassett
[ Poster] 
None
Detecting structured signals in radio telescope data using RKHS
In Machine Learning and the Physical Sciences
Russell Tsuchida · Suk Yee Yong
[ Poster] 
None
Domain Adaptation for Simulation-Based Dark Matter Searches with Strong Gravitational Lensing
In Machine Learning and the Physical Sciences
Pranath Reddy Kumbam · Sergei Gleyzer · Michael Toomey · Marcos Tidball
[ Poster] 
None
A hybrid Reduced Basis and Machine-Learning algorithm for building Surrogate Models: a first application to electromagnetism
In Machine Learning and the Physical Sciences
Alejandro Ribes · Ruben Persicot · Lucas Meyer · Jean-Pierre Ducreux
None
Data-driven discovery of non-Newtonian astronomy via learning non-Euclidean Hamiltonian
In Machine Learning and the Physical Sciences
Oswin So · Gongjie Li · Evangelos Theodorou · Molei Tao
[ Poster] 
None
Multi-scale Digital Twin: Developing a fast and physics-infused surrogate model for groundwater contamination with uncertain climate models
In Machine Learning and the Physical Sciences
Lijing Wang · Takuya Kurihana · Aurelien Meray · Ilijana Mastilovic · Satyarth Praveen · Zexuan Xu · Milad Memarzadeh · Alexander Lavin · Haruko Wainwright
[ Poster] 
None
Normalizing Flows for Fragmentation and Hadronization
In Machine Learning and the Physical Sciences
Ahmed Youssef · Philip Ilten · Tony Menzo · Jure Zupan · Manuel Szewc · Stephen Mrenna · Michael K. Wilkinson
[ Poster] 
None
GAN-Flow: A dimension-reduced variational framework for physics-based inverse problems
In Machine Learning and the Physical Sciences
Agnimitra Dasgupta · Dhruv Patel · Deep Ray · Erik Johnson · Assad Oberai
[ Poster] 
None
Control and Calibration of GlueX Central Drift Chamber Using Gaussian Process Regression
In Machine Learning and the Physical Sciences
Diana McSpadden · Torri Jeske · Naomi Jarvis · David Lawrence · Thomas Britton · nikhil kalra
[ Poster] 
None
The Senseiver: attention-based global field reconstruction from sparse observations
In Machine Learning and the Physical Sciences
Javier E. Santos · Zachary Fox · Arvind Mohan · Hari Viswanathan · NIcholas Lubbers
[ Poster] 
None
Skip Connections for High Precision Regressors
In Machine Learning and the Physical Sciences
Ayan Paul · Fady Bishara · Jennifer Dy
[ Poster] 
None
Likelihood-Free Frequentist Inference for Calorimetric Muon Energy Measurement in High-Energy Physics
In Machine Learning and the Physical Sciences
Luca Masserano · Ann Lee · Rafael Izbicki · Mikael Kuusela · tommaso dorigo
[ Poster] 
None
Uncertainty Aware Deep Learning for Particle Accelerators
In Machine Learning and the Physical Sciences
Kishansingh Rajput · Malachi Schram · Karthik Somayaji NS
[ Poster] 
None
Geometric NeuralPDE (GNPnet) Models for Learning Dynamics
In Machine Learning and the Physical Sciences
Oluwadamilola Fasina · Smita Krishnaswamy · Aditi Krishnapriyan
[ Poster] 
None
PIPS: Path Integral Stochastic Optimal Control for Path Sampling in Molecular Dynamics
In Machine Learning and the Physical Sciences
Lars Holdijk · Yuanqi Du · Ferry Hooft · Priyank Jaini · Berend Ensing · Max Welling
None
A Self-Supervised Approach to Reconstruction in Sparse X-Ray Computed Tomography
In Machine Learning and the Physical Sciences
Rey Mendoza · Minh Nguyen · Judith Weng Zhu · Talita Perciano · Vincent Dumont · Juliane Mueller · Vidya Ganapati
[ Poster] 
None
Energy based models for tomography of quantum spin-lattice systems
In Machine Learning and the Physical Sciences
Abhijith Jayakumar · Marc Vuffray · Andrey Lokhov
[ Poster] 
None
Elements of effective machine learning datasets in astronomy
In Machine Learning and the Physical Sciences
Bernie Boscoe · Tuan Do
[ Poster] 
None
Adversarial Noise Injection for Learned Turbulence Simulations
In Machine Learning and the Physical Sciences
Jingtong Su · Julia Kempe · Drummond Fielding · Nikolaos Tsilivis · Miles Cranmer · Shirley Ho
[ Poster] 
None
Shining light on data
In Machine Learning and the Physical Sciences
Akshat Kumar · Mohan Sarovar
[ Poster] 
None
A Novel Automatic Mixed Precision Approach For Physics Informed Training
In Machine Learning and the Physical Sciences
Jinze Xue · Akshay Subramaniam · Mark Hoemmen
[ Poster] 
None
Atmospheric retrievals of exoplanets using learned parameterizations of pressure-temperature profiles
In Machine Learning and the Physical Sciences
Timothy Gebhard · Daniel Angerhausen · Björn Konrad · Eleonora Alei · Sascha Quanz · Bernhard Schölkopf
[ Poster] 
None
Probabilistic Mixture Modeling For End-Member Extraction in Hyperspectral Data
In Machine Learning and the Physical Sciences
Oliver Hoidn · Aashwin Mishra · Apurva Mehta
[ Poster] 
None
Posterior samples of source galaxies in strong gravitational lenses with score-based priors
In Machine Learning and the Physical Sciences
Alexandre Adam · Adam Coogan · Nikolay Malkin · Ronan Legin · Laurence Perreault-Levasseur · Yashar Hezaveh · Yoshua Bengio
None
Learning Integrable Dynamics with Action-Angle Networks
In Machine Learning and the Physical Sciences
Ameya Daigavane · Arthur Kosmala · Miles Cranmer · Tess Smidt · Shirley Ho
[ Poster] 
None
Physics-informed Bayesian Optimization of an Electron Microscope
In Machine Learning and the Physical Sciences
Desheng Ma
None
Emulating cosmological multifields with generative adversarial networks
In Machine Learning and the Physical Sciences
Sambatra Andrianomena · Sultan Hassan · Francisco Villaescusa-Navarro
[ Poster] 
None
Physics-informed neural networks for modeling rate- and temperature-dependent plasticity
In Machine Learning and the Physical Sciences
Rajat Arora · Pratik Kakkar · Amit Chakraborty · Biswadip Dey
[ Poster] 
None
A Trust Crisis In Simulation-Based Inference? Your Posterior Approximations Can Be Unfaithful
In Machine Learning and the Physical Sciences
Joeri Hermans · Arnaud Delaunoy · François Rozet · Antoine Wehenkel · Volodimir Begy · Gilles Louppe
[ Poster] 
None
Modeling halo and central galaxy orientations on the SO(3) manifold with score-based generative models
In Machine Learning and the Physical Sciences
Yesukhei Jagvaral · Francois Lanusse · Rachel Mandelbaum
[ Poster] 
None
Geometric path augmentation for inference of sparsely observed stochastic nonlinear systems
In Machine Learning and the Physical Sciences
Dimitra Maoutsa
[ Poster] 
None
A probabilistic deep learning model to distinguish cusps and cores in dwarf galaxies
In Machine Learning and the Physical Sciences
Julen Expósito Márquez · Marc Huertas-Company · Arianna Di Cintio · Chris Brook · Andrea Macciò · Rob Grant · Elena Arjona
[ Poster] 
None
Multi-Fidelity Transfer Learning for accurate database PDE approximation
In Machine Learning and the Physical Sciences
Wenzhuo LIU · Mouadh Yagoubi · Marc Schoenauer · David Danan
[ Poster] 
None
Adaptive Selection of Atomic Fingerprints for High-Dimensional Neural Network Potentials
In Machine Learning and the Physical Sciences
Johannes Sandberg · Emilie Devijver · Noel Jakse · Thomas Voigtmann
[ Poster] 
None
HyperFNO: Improving the Generalization Behavior of Fourier Neural Operators
In Machine Learning and the Physical Sciences
Francesco Alesiani · Makoto Takamoto · Mathias Niepert
[ Poster] 
None
Fast kinematics modeling for conjunction with lens image modeling
In Machine Learning and the Physical Sciences
Matthew Gomer · Luca Biggio · Sebastian Ertl · Han Wang · Aymeric Galan · Lyne Van de Vyvere · Dominique Sluse · Georgios Vernardos · Sherry Suyu
[ Poster] 
None
Continual learning autoencoder training for a particle-in-cell simulation via streaming
In Machine Learning and the Physical Sciences
Patrick Stiller · Varun Makdani · Franz Poeschel · Richard Pausch · Alexander Debus · Michael Bussmann · Nico Hoffmann
[ Poster] 
None
HGPflow: Particle reconstruction as hyperedge prediction
In Machine Learning and the Physical Sciences
Etienne Dreyer · Nilotpal Kakati · Francesco Armando Di Bello
[ Poster] 
None
Diversity Balancing Generative Adversarial Networks for fast simulation of the Zero Degree Calorimeter in the ALICE experiment at CERN
In Machine Learning and the Physical Sciences
Jan Dubiński · Kamil Deja · Sandro Wenzel · Przemysław Rokita · Tomasz Trzcinski
[ Poster] 
None
Identifying Hamiltonian Manifold in Neural Networks
In Machine Learning and the Physical Sciences
Yeongwoo Song · Hawoong Jeong
[ Poster] 
None
Physics-Informed Neural Networks as Solvers for the Time-Dependent Schrödinger Equation
In Machine Learning and the Physical Sciences
Karan Shah · Patrick Stiller · Nico Hoffmann · Attila Cangi
[ Poster] 
None
A physics-informed search for metric solutions to Ricci flow, their embeddings, and visualisation
In Machine Learning and the Physical Sciences
Aarjav Jain · Challenger Mishra · Pietro Lió
[ Poster] 
None
Towards solving model bias in cosmic shear forward modeling
In Machine Learning and the Physical Sciences
Benjamin Remy · Francois Lanusse · Jean-Luc Starck
[ Poster] 
None
Physical Data Models in Machine Learning Imaging Pipelines
In Machine Learning and the Physical Sciences
Marco Aversa · Luis Oala · Christoph Clausen · Roderick Murray-Smith · Bruno Sanguinetti
[ Poster] 
None
Amortized Bayesian Inference of GISAXS Data with Normalizing Flows
In Machine Learning and the Physical Sciences
Maksim Zhdanov · Lisa Randolph · Thomas Kluge · Motoaki Nakatsutsumi · Christian Gutt · Marina Ganeva · Nico Hoffmann
[ Poster] 
None
Addressing out-of-distribution data for flow-based gravitational wave inference
In Machine Learning and the Physical Sciences
Maximilian Dax · Stephen Green · Jonas Wildberger · Jonathan Gair · Michael Puerrer · Jakob Macke · Alessandra Buonanno · Bernhard Schölkopf
[ Poster] 
None
Applications of Differentiable Physics Simulations in Particle Accelerator Modeling
In Machine Learning and the Physical Sciences
Ryan Roussel · Auralee Edelen
[ Poster] 
None
Monte Carlo Techniques for Addressing Large Errors and Missing Data in Simulation-based Inference
In Machine Learning and the Physical Sciences
Bingjie Wang · Joel Leja · Victoria Villar · Joshua Speagle
[ Poster] 
None
Topological Jet Tagging
In Machine Learning and the Physical Sciences
Dawson Thomas · Sarah Demers · Smita Krishnaswamy · Bastian Rieck
[ Poster] 
None
DiffDock: Diffusion Steps, Twists, and Turns for Molecular Docking
In Machine Learning and the Physical Sciences
Gabriele Corso · Hannes Stärk · Bowen Jing · Regina Barzilay · Tommi Jaakkola
[ Poster] 
None
Decay-aware neural network for event classification in collider physics
In Machine Learning and the Physical Sciences
Tomoe Kishimoto · Masahiro Morinaga · Masahiko Saito · Junichi Tanaka
[ Poster] 
None
Phase transitions and structure formation in learning local rules
In Machine Learning and the Physical Sciences
Bojan Žunkovič · Enej Ilievski
[ Poster] 
None
ML4LM: Machine Learning for Safely Landing on Mars
In Machine Learning and the Physical Sciences
David Wu · Wai Tong Chung · Matthias Ihme
[ Poster] 
None
Amortized Bayesian Inference for Supernovae in the Era of the Vera Rubin Observatory Using Normalizing Flows
In Machine Learning and the Physical Sciences
Victoria Villar
[ Poster] 
None
Scalable Bayesian Inference for Finding Strong Gravitational Lenses
In Machine Learning and the Physical Sciences
Yash Patel · Jeffrey Regier
[ Poster] 
None
Neuro-Symbolic Partial Differential Equation Solver
In Machine Learning and the Physical Sciences
Pouria Akbari Mistani · Samira Pakravan · Rajesh Ilango · Sanjay Choudhry · Frederic Gibou
[ Poster] 
None
From Particles to Fluids: Dimensionality Reduction for Non-Maxwellian Plasma Velocity Distributions Validated in the Fluid Context
In Machine Learning and the Physical Sciences
Daniel da Silva
[ Poster] 
None
Neural Network-based Real-Time Parameter Estimation in Electrochemical Sensors with Unknown Confounding Factors
In Machine Learning and the Physical Sciences
Sarthak Jariwala
[ Poster] 
None
Emulating Fast Processes in Climate Models
In Machine Learning and the Physical Sciences
Noah Brenowitz · W. Andre Perkins · Jacqueline M. Nugent · Oliver Watt-Meyer · Spencer K. Clark · Anna Kwa · Brian Henn · Jeremy McGibbon · Christopher S. Bretherton
[ Poster] 
None
GAUCHE: A Library for Gaussian Processes in Chemistry
In Machine Learning and the Physical Sciences
Ryan-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
[ Poster] 
None
Wavelets Beat Monkeys at Adversarial Robustness
In Machine Learning and the Physical Sciences
Jingtong Su · Julia Kempe
[ Poster] 
None
First principles physics-informed neural network for quantum wavefunctions and eigenvalue surfaces
In Machine Learning and the Physical Sciences
Marios Mattheakis · Gabriel R. Schleder · Daniel Larson · Efthimios Kaxiras
[ Poster] 
None
Neural Inference of Gaussian Processes for Time Series Data of Quasars
In Machine Learning and the Physical Sciences
Egor Danilov · Aleksandra Ciprijanovic · Brian Nord
[ Poster] 
None
Strong-Lensing Source Reconstruction with Denoising Diffusion Restoration Models
In Machine Learning and the Physical Sciences
Konstantin Karchev · Noemi Anau Montel · Adam Coogan · Christoph Weniger
[ Poster] 
None
Score-based Seismic Inverse Problems
In Machine Learning and the Physical Sciences
Sriram Ravula · Dimitri Voytan · Elad Liebman · Ram Tuvi · Yash Gandhi · Hamza Ghani · Alex Ardel · Mrinal Sen · Alex Dimakis
[ Poster] 
None
Deep-pretrained-FWI: combining supervised learning with physics-informed neural network
In Machine Learning and the Physical Sciences
ANA PAULA MULLER · Clecio Roque Bom · Jessé Carvalho Costa · Elisângela Lopes Faria · Marcelo Portes de Albuquerque · Marcio Portes de Albuquerque
[ Poster] 
None
Strong Lensing Parameter Estimation on Ground-Based Imaging Data Using Simulation-Based Inference
In Machine Learning and the Physical Sciences
Jason Poh · Ashwin Samudre · Aleksandra Ciprijanovic · Brian Nord · Joshua Frieman · Gourav Khullar
[ Poster] 
None
Recovering Galaxy Cluster Convergence from Lensed CMB with Generative Adversarial Networks
In Machine Learning and the Physical Sciences
Liam Parker · Dongwon Han · Shirley Ho · Pablo Lemos
[ Poster] 
None
Differentiable Physics-based Greenhouse Simulation
In Machine Learning and the Physical Sciences
Nhat M. Nguyen · Hieu Tran · Minh Duong · Hanh Bui · Kenneth Tran
[ Slides]  [ Poster] 
None
ClimFormer - a Spherical Transformer model for long-term climate projections
In Machine Learning and the Physical Sciences
Salva Rühling Cachay · Peetak Mitra · Sookyung Kim · Subhashis Hazarika · Haruki Hirasawa · Dipti Hingmire · Hansi Singh · Kalai Ramea
[ Poster] 
None
Computing the Bayes-optimal classifier and exact maximum likelihood estimator with a semi-realistic generative model for jet physics
In Machine Learning and the Physical Sciences
Kyle Cranmer · Matthew Drnevich · Lauren Greenspan · Sebastian Macaluso · Duccio Pappadopulo
[ Poster] 
None
PELICAN: Permutation Equivariant and Lorentz Invariant or Covariant Aggregator Network for Particle Physics
In Machine Learning and the Physical Sciences
Jan Offermann · Alexander Bogatskiy · Timothy Hoffman · David W Miller
[ Poster] 
None
FO-PINNs: A First-Order formulation for Physics~Informed Neural Networks
In Machine Learning and the Physical Sciences
Rini Jasmine Gladstone · Mohammad Amin Nabian · Hadi Meidani
[ Poster] 
None
Predicting Full-Field Turbulent Flows Using Fourier Neural Operator
In Machine Learning and the Physical Sciences
Peter Renn · Sahin Lale · Cong Wang · Zongyi Li · Anima Anandkumar · Morteza Gharib
[ Poster] 
None
Why are deep learning-based models of geophysical turbulence long-term unstable?
In Machine Learning and the Physical Sciences
Ashesh Chattopadhyay · Pedram Hassanzadeh
[ Poster] 
None
Graph Structure from Point Clouds: Geometric Attention is All You Need
In Machine Learning and the Physical Sciences
Daniel Murnane
[ Poster] 
None
One-Class Dense Networks for Anomaly Detection
In Machine Learning and the Physical Sciences
Norman Karr · Benjamin Nachman · David Shih
[ Poster] 
None
Self-supervised detection of atmospheric phenomena from remotely sensed synthetic aperture radar imagery
In Machine Learning and the Physical Sciences
Yannik Glaser · Peter Sadowski · Justin Stopa
[ Poster] 
None
Anomaly Detection with Multiple Reference Datasets in High Energy Physics
In Machine Learning and the Physical Sciences
Mayee Chen · Benjamin Nachman · Frederic Sala
[ Poster] 
None
Using Shadows to Learn Ground State Properties of Quantum Hamiltonians
In Machine Learning and the Physical Sciences
Viet T. Tran · Laura Lewis · Johannes Kofler · Hsin-Yuan Huang · Richard Kueng · Sepp Hochreiter · Sebastian Lehner
[ Poster] 
None
Score Matching via Differentiable Physics
In Machine Learning and the Physical Sciences
Benjamin Holzschuh · Simona Vegetti · Nils Thuerey
[ Poster] 
None
On Using Deep Learning Proxies as Forward Models in Optimization Problems
In Machine Learning and the Physical Sciences
Fatima Albreiki · Nidhal Belayouni · Deepak Gupta
[ Poster] 
None
Do Better QM9 Models Extrapolate as Better Quantum Chemical Property Predictors?
In Machine Learning and the Physical Sciences
YUCHENG ZHANG · Nontawat Charoenphakdee · So Takamoto
[ Poster] 
None
Time-aware Bayesian optimization for adaptive particle accelerator tuning
In Machine Learning and the Physical Sciences
Nikita Kuklev · Yine Sun · Hairong Shang · Michael Borland · Gregory Fystro
[ Poster] 
None
Inferring molecular complexity from mass spectrometry data using machine learning
In Machine Learning and the Physical Sciences
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
[ Poster] 
None
Detection is truncation: studying source populations with truncated marginal neural ratio estimation
In Machine Learning and the Physical Sciences
Noemi Anau Montel · Christoph Weniger
[ Poster] 
None
Cosmology from Galaxy Redshift Surveys with PointNet
In Machine Learning and the Physical Sciences
Sotiris Anagnostidis · Arne Thomsen · Alexandre Refregier · Tomasz Kacprzak · Luca Biggio · Thomas Hofmann · Tilman Tröster
[ Slides]  [ Poster] 
None
Learning Similarity Metrics for Volumetric Simulations with Multiscale CNNs
In Machine Learning and the Physical Sciences
Georg Kohl · Liwei Chen · Nils Thuerey
[ Poster] 
None
CaloMan: Fast generation of calorimeter showers with density estimation on learned manifolds
In Machine Learning and the Physical Sciences
Jesse Cresswell · Brendan Ross · Gabriel Loaiza-Ganem · Humberto Reyes-Gonzalez · Marco Letizia · Anthony Caterini
[ Poster] 
None
Graphical Models are All You Need: Per-interaction reconstruction uncertainties in a dark matter detection experiment
In Machine Learning and the Physical Sciences
Christina Peters · Aaron Higuera · Shixiao Liang · Waheed Bajwa · Christopher Tunnell
[ Poster] 
None
SE(3)-equivariant self-attention via invariant features
In Machine Learning and the Physical Sciences
Nan Chen · Soledad Villar
[ Poster] 
None
Physics-Driven Convolutional Autoencoder Approach for CFD Data Compressions
In Machine Learning and the Physical Sciences
Alberto Olmo · Ahmed Zamzam · Andrew Glaws · Ryan King
[ Poster] 
None
Deconvolving Detector Effects for Distribution Moments
In Machine Learning and the Physical Sciences
Krish Desai · Benjamin Nachman · Jesse Thaler
[ Poster] 
None
Deep Learning Modeling of Subgrid Physics in Cosmological N-body Simulations
In Machine Learning and the Physical Sciences
Georgios Markos Chatziloizos · Francois Lanusse · Tristan Cazenave
[ Poster] 
None
Applying Deep Reinforcement Learning to the HP Model for Protein Structure Prediction
In Machine Learning and the Physical Sciences
Kaiyuan Yang · Houjing Huang · Olafs Vandans · Adithyavairavan Murali · Fujia Tian · Roland Yap · Liang Dai
[ Poster] 
None
Learning dynamical systems: an example from open quantum system dynamics.
In Machine Learning and the Physical Sciences
Pietro Novelli
None
Flexible learning of quantum states with generative query neural networks
In Machine Learning and the Physical Sciences
Yan Zhu · Ya-Dong Wu · Ge Bai · Dong-Sheng Wang · Yuexuan Wang · Giulio Chiribella
None
Towards Creating Benchmark Datasets of Universal Neural Network Potential for Material Discovery
In Machine Learning and the Physical Sciences
So Takamoto · Chikashi Shinagawa · Nontawat Charoenphakdee
[ Poster] 
None
Finding active galactic nuclei through Fink
In Machine Learning and the Physical Sciences
Etienne Russeil · Emille Ishida · Julien Peloton · Anais Möller · Roman Le Montagner
[ Poster] 
None
A fast and flexible machine learning approach to data quality monitoring
In Machine Learning and the Physical Sciences
Marco Letizia · Gaia Grosso · Andrea Wulzer · Marco Zanetti · Jacopo Pazzini · Marco Rando · Nicolò Lai
[ Poster] 
None
Normalizing Flows for Hierarchical Bayesian Analysis: A Gravitational Wave Population Study
In Machine Learning and the Physical Sciences
David Ruhe · Kaze Wong · Miles Cranmer · Patrick Forré
[ Poster] 
None
How good is the Standard Model? Machine learning multivariate Goodness of Fit tests
In Machine Learning and the Physical Sciences
Gaia Grosso · Marco Letizia · Andrea Wulzer · Maurizio Pierini
[ Poster] 
None
Particle-level Compression for New Physics Searches
In Machine Learning and the Physical Sciences
Yifeng Huang · Jack Collins · Benjamin Nachman · Simon Knapen · Daniel Whiteson
[ Poster] 
None
Towards a non-Gaussian Generative Model of large-scale Reionization Maps
In Machine Learning and the Physical Sciences
Yu-Heng Lin · Sultan Hassan · Bruno Régaldo-Saint Blancard · Michael Eickenberg · Chirag Modi
[ Poster] 
None
Learning the nonlinear manifold of extreme aerodynamics
In Machine Learning and the Physical Sciences
Kai Fukami · Kunihiko Taira
[ Poster] 
None
Can denoising diffusion probabilistic models generate realistic astrophysical fields?
In Machine Learning and the Physical Sciences
Nayantara Mudur · Douglas P. Finkbeiner
[ Poster] 
None
Plausible Adversarial Attacks on Direct Parameter Inference Models in Astrophysics
In Machine Learning and the Physical Sciences
Benjamin Horowitz · Peter Melchior
[ Poster] 
None
Source Identification and Field Reconstruction of Advection-Diffusion Process from Sparse Sensor Measurements
In Machine Learning and the Physical Sciences
Arka Daw · Kyongmin Yeo · Anuj Karpatne ·
[ Poster] 
None
Statistical Inference for Coadded Astronomical Images
In Machine Learning and the Physical Sciences
Mallory Wang · Ismael Mendoza · Jeffrey Regier · Camille Avestruz · Cheng Wang
[ Poster] 
None
Differentiable composition for model discovery
In Machine Learning and the Physical Sciences
Omer Rochman Sharabi · Gilles Louppe
[ Poster] 
None
DIGS: Deep Inference of Galaxy Spectra with Neural Posterior Estimation
In Machine Learning and the Physical Sciences
Gourav Khullar · Brian Nord · Aleksandra Ciprijanovic · Jason Poh · Fei Xu · Ashwin Samudre
[ Poster] 
None
Tensor networks for active inference with discrete observation spaces
In Machine Learning and the Physical Sciences
Samuel T. Wauthier · Bram Vanhecke · Tim Verbelen · Bart Dhoedt
[ Poster] 
None
Employing CycleGANs to Generate Realistic STEM Images for Machine Learning
In Machine Learning and the Physical Sciences
Abid Khan · Chia-Hao Lee · Pinshane Y. Huang · Bryan Clark
[ Poster] 
None
CAPE: Channel-Attention-Based PDE Parameter Embeddings for SciML
In Machine Learning and the Physical Sciences
Makoto Takamoto · Francesco Alesiani · Mathias Niepert
[ Poster] 
None
One-shot learning for solution operators of partial differential equations
In Machine Learning and the Physical Sciences
Lu Lu · Anran Jiao · Jay Pathak · Rishikesh Ranade · Haiyang He
[ Poster] 
None
Simplifying Polylogarithms with Machine Learning
In Machine Learning and the Physical Sciences
Aurelien Dersy · Matthew Schwartz · Xiaoyuan Zhang
[ Poster] 
None
Machine learning for complete intersection Calabi-Yau manifolds
In Machine Learning and the Physical Sciences
Harold Erbin · Mohamed Tamaazousti · Riccardo Finotello
[ Poster] 
None
Generating Calorimeter Showers as Point Clouds
In Machine Learning and the Physical Sciences
Simon Schnake · Dirk Krücker · Kerstin Borras
[ Poster] 
None
SuNeRF: Validation of a 3D Global Reconstruction of the Solar Corona Using Simulated EUV Images
In Machine Learning and the Physical Sciences
Kyriaki-Margarita Bintsi · Robert Jarolim · Benoit Tremblay · Miraflor Santos · Anna Jungbluth · James Mason · Sairam Sundaresan · Angelos Vourlidas · Cooper Downs · Ronald Caplan · Andres Munoz-Jaramillo
[ Poster] 
None
Machine-learned climate model corrections from a global storm-resolving model
In Machine Learning and the Physical Sciences
Anna Kwa
[ Poster] 
None
Transfer Learning with Physics-Informed Neural Networks for Efficient Simulation of Branched Flows
In Machine Learning and the Physical Sciences
Raphael Pellegrin · Blake Bullwinkel · Marios Mattheakis · Pavlos Protopapas
[ Poster] 
None
Ad-hoc Pulse Shape Simulation using Cyclic Positional U-Net
In Machine Learning and the Physical Sciences
Aobo Li
[ Poster] 
None
Virgo: Scalable Unsupervised Classification of Cosmological Shock Waves
In Machine Learning and the Physical Sciences
Max Lamparth · Ludwig Böss · Ulrich Steinwandel · Klaus Dolag
[ Poster] 
None
Set-Conditional Set Generation for Particle Physics
In Machine Learning and the Physical Sciences
Sanmay Ganguly · Lukas Heinrich · Nilotpal Kakati · Nathalie Soybelman
[ Poster] 
None
Lyapunov Regularized Forecaster
In Machine Learning and the Physical Sciences
Rong Zheng · Rose Yu
[ Poster] 
None
Super-resolving Dark Matter Halos using Generative Deep Learning
In Machine Learning and the Physical Sciences
David Schaurecker
[ Poster] 
None
Astronomical Image Coaddition with Bundle-Adjusting Radiance Fields
In Machine Learning and the Physical Sciences
Harlan Hutton · Harshitha Palegar · Shirley Ho · Miles Cranmer · Peter Melchior · Jenna Eubank
[ Poster] 
None
Certified data-driven physics-informed greedy auto-encoder simulator
In Machine Learning and the Physical Sciences
Xiaolong He · Youngsoo Choi · William Fries · Jon Belof · Jiun-Shyan Chen
[ Poster] 
None
Discovering Long-period Exoplanets using Deep Learning with Citizen Science Labels
In Machine Learning and the Physical Sciences
Shreshth A Malik · Nora Eisner · Chris Lintott · Yarin Gal
[ Poster] 
None
Insight into cloud processes from unsupervised classification with a rotation-invariant autoencoder
In Machine Learning and the Physical Sciences
Takuya Kurihana · James Franke · Ian Foster · Ziwei Wang · Elisabeth Moyer
[ Poster] 
None
Learning Electron Bunch Distribution along a FEL Beamline by Normalising Flows
In Machine Learning and the Physical Sciences
Anna Willmann · Jurjen Pieter Couperus Cabadağ · Yen-Yu Chang · Richard Pausch · Amin Ghaith · Alexander Debus · Arie Irman · Michael Bussmann · Ulrich Schramm · Nico Hoffmann
[ Poster] 
None
Closing the resolution gap in Lyman alpha simulations with deep learning
In Machine Learning and the Physical Sciences
Cooper Jacobus · Peter Harrington · Zarija Lukić
[ Poster] 
None
Decorrelation with Conditional Normalizing Flows
In Machine Learning and the Physical Sciences
Samuel Klein · Tobias Golling
[ Poster] 
None
Real-time Health Monitoring of Heat Exchangers using Hypernetworks and PINNs
In Machine Learning and the Physical Sciences
Ritam Majumdar · Vishal Jadhav · Anirudh Deodhar · Shirish Karande · Lovekesh Vig · Venkataramana Runkana
[ Poster] 
None
Galaxy Morphological Classification with Deformable Attention Transformer
In Machine Learning and the Physical Sciences
SEOKUN KANG · Min-Su Shin · Taehwan Kim
[ Poster] 
None
Learning-based solutions to nonlinear hyperbolic PDEs: Empirical insights on generalization errors
In Machine Learning and the Physical Sciences
Bilal Thonnam Thodi · Sai Venkata Ramana Ambadipudi · Saif Eddin Jabari
[ Poster] 
None
Geometry-aware Autoregressive Models for Calorimeter Shower Simulations
In Machine Learning and the Physical Sciences
Junze Liu · Aishik Ghosh · Dylan Smith · Pierre Baldi · Daniel Whiteson
[ Poster] 
None
De-noising non-Gaussian fields in cosmology with normalizing flows
In Machine Learning and the Physical Sciences
Adam Rouhiainen · Moritz Münchmeyer
[ Poster] 
None
Emulating cosmological growth functions with B-Splines
In Machine Learning and the Physical Sciences
Ngai Pok Kwan · Chirag Modi · Yin Li · Shirley Ho
[ Poster] 
None
Simulation-based inference of the 2D ex-situ stellar mass fraction distribution of galaxies using variational autoencoders
In Machine Learning and the Physical Sciences
Eirini Angeloudi · Marc Huertas-Company · Jesús Falcón-Barroso · Regina Sarmiento · Daniel Walo-Martín · Annalisa Pillepich · Jesús Vega Ferrero
[ Poster] 
None
Physics solutions for privacy leaks in machine learning
In Machine Learning and the Physical Sciences
Alejandro Pozas-Kerstjens · Senaida Hernandez-Santana · José Ramón Pareja Monturiol · Marco Castrillon Lopez · Giannicola Scarpa · Carlos E. Gonzalez-Guillen · David Perez-Garcia
[ Poster] 
None
Uncertainty quantification methods for ML-based surrogate models of scientific applications
In Machine Learning and the Physical Sciences
Kishore Basu · Yujia Hao · Delphine Hintz · Dev Shah · Aaron Palmer · Gurpreet Singh Hora · Darian Nwankwo · Laurent White
[ Poster] 
None
Improved Training of Physics-informed Neural Networks using Energy-Based priors: A Study on Electrical Impedance Tomography
In Machine Learning and the Physical Sciences
Akarsh Pokkunuru · Pedram Rooshenas · Thilo Strauss · Anuj Abhishek · Taufiquar Khan
[ Poster] 
None
Intra-Event Aware Imitation Game for Fast Detector Simulation
In Machine Learning and the Physical Sciences
Hosein Hashemi · Nikolai Hartmann · Sahand Sharifzadeh · James Kahn · Thomas Kuhr
[ Poster] 
None
Improving Generalization with Physical Equations
In Machine Learning and the Physical Sciences
Antoine Wehenkel · Jens Behrmann · Hsiang Hsu · Guillermo Sapiro · Gilles Louppe · Joern-Henrik Jacobsen
[ Poster] 
None
Point Cloud Generation using Transformer Encoders and Normalising Flows
In Machine Learning and the Physical Sciences
Benno Käch · Dirk Krücker · Isabell Melzer
[ Poster] 
None
Leveraging the Stochastic Predictions of Bayesian Neural Networks for Fluid Simulations
In Machine Learning and the Physical Sciences
Maximilian Mueller · Robin Greif · Frank Jenko · Nils Thuerey
[ Poster] 
None
HIGlow: Conditional Normalizing Flows for High-Fidelity HI Map Modeling
In Machine Learning and the Physical Sciences
Roy Friedman · Sultan Hassan
[ Poster] 
None
Learning Feynman Diagrams using Graph Neural Networks
In Machine Learning and the Physical Sciences
Alexander Norcliffe · Harrison Mitchell · Pietro Lió
[ Slides]  [ Poster] 
None
Physics-Informed Machine Learning of Dynamical Systems for Efficient Bayesian Inference
In Machine Learning and the Physical Sciences
Som Dhulipala · Yifeng Che · Michael Shields
[ Poster] 
None
Offline Model-Based Reinforcement Learning for Tokamak Control
In Machine Learning and the Physical Sciences
Ian Char · Joseph Abbate · Laszlo Bardoczi · Mark Boyer · Youngseog Chung · Rory Conlin · Keith Erickson · Viraj Mehta · Nathan Richner · Egemen Kolemen · Jeff Schneider
[ Poster] 
None
Learning Uncertainties the Frequentist Way: Calibration and Correlation in High Energy Physics
In Machine Learning and the Physical Sciences
Rikab Gambhir · Jesse Thaler · Benjamin Nachman
[ Poster]