Skip to yearly menu bar Skip to main content


Workshop

D3S3: Data-driven and Differentiable Simulations, Surrogates, and Solvers

Tribhuvanesh Orekondy ⋅ Arash Behboodi ⋅ Emilia Magnani ⋅ Philipp Hennig ⋅ Paris Perdikaris ⋅ Kamyar Azizzadenesheli
Project Page [ OpenReview] 

Abstract

Video

Chat is not available.

Schedule

Timezone: America/Los_Angeles
8:30 AM
Johannes Brandstetter (JKU Linz)
Johannes Brandstetter
9:00 AM
Coffee Break
9:30 AM
Nicholas Nelsen (MIT)
Nicholas Nelsen
10:00 AM
Anima Anandkumar (CalTech)
Anima Anandkumar
10:30 AM
Sherry Yang (Google Deepmind)
Sherry Yang
11:00 AM
Poster Session
12:00 PM
Lunch Break
1:00 PM
Nicholas Krämer (Denmark’s TU)
Nicholas Krämer
1:30 PM
Manmohan Chandraker (UC San Diego)
Manmohan Chandraker
2:00 PM
Panel Discussion
Ilze Amanda Auzina ⋅ Johannes Brandstetter ⋅ Sherry Yang ⋅ Daniel Worrall ⋅ Nicholas Nelsen ⋅ Manmohan Chandraker
3:00 PM
Coffee Break
3:30 PM
Daniel Worrall (Google Deepmind)
Daniel Worrall
4:00 PM
Ilze Amanad Auzina (U v Amsterdam)
Ilze Amanda Auzina
4:30 PM
Closing Remarks
Learning SDE Solutions with Neural Stochastic Flows
Naoki Kiyohara ⋅ Edward Johns ⋅ Yingzhen Li
Optimizing the IFMIF-DONES Particle Accelerator with Differentiable Deep Learning Surrogate Models
Galo Gallardo ⋅ Guillermo Rodriguez Llorente ⋅ Lucas Magariños ⋅ Rodrigo Morant Navascués ⋅ Nikita Kkhvatkin Petrovsky ⋅ Roberto Gómez-Espinosa Martín
VehicleSDF: A 3D generative model for constrained engineering design via surrogate modeling
Hayata Morita ⋅ Kohei Shintani ⋅ Chenyang Yuan ⋅ Frank Permenter
Spatial Shortcuts in Graph Neural Controlled Differential Equations
Michael Detzel ⋅ Gabriel Nobis ⋅ Jackie Ma ⋅ Wojciech Samek
ParaFIND: Parameter Field Inference on Non-uniform Domains using Neural Network
Masmoudi ⋅ Xuyang Li ⋅ Nizar Lajnef ⋅ Vishnu Boddeti
Projected Neural Differential Equations for Power Grid Modeling with Constraints
Alistair White ⋅ Anna Büttner ⋅ Maximilian Gelbrecht ⋅ Niki Kilbertus ⋅ Frank Hellmann ⋅ Niklas Boers
Projected Low-Rank Gradient in Diffusion-based Models for Inverse Problems
Rayhan Zirvi ⋅ Bahareh Tolooshams ⋅ Animashree Anandkumar
Neural Operators as Fast Surrogate Models for the Transmission Loss of Parameterized Sonic Crystals
Jakob Wagner ⋅ Samuel Burbulla ⋅ Miguel de Benito Delgado ⋅ Johannes Schmid
ADAGE-Diff: Two-level adaptive agent based modelling for differentiable policy design
Benjamin Patrick Evans ⋅ Sihan Zeng ⋅ Sumitra Ganesh ⋅ Leo Ardon
Surrogate-based Physical Error Correction for Spectroscopy Quantification
ruiyuan kang ⋅ Panagiotis Liatsis ⋅ Meixia Geng ⋅ Qingjie Yang
Model Exploration through Marginal Likelihood Entropy Maximisation
Daniel Jarne Ornia ⋅ Joel Dyer ⋅ Nicholas Bishop ⋅ Anisoara Calinescu ⋅ Michael Wooldridge
Efficient Probabilistic Modeling of Crystallization at Mesoscopic Scale
Pol Timmer ⋅ Koen Minartz ⋅ Vlado Menkovski
Gradient of Clifford Neural Networks
Takashi Maruyama ⋅ Francesco Alesiani
A neural surrogate solver for radiation transfer
Aleksei Sorokin ⋅ Xiaoyi Lu ⋅ Yi Wang
GLEAM-AI: Neural Surrogate for Accelerated Epidemic Analytics and Forecasting
Mohammadmehdi Zahedi ⋅ Dongxia Wu ⋅ Jessica Davis ⋅ Yian Ma ⋅ Alessandro Vespignani ⋅ Rose Yu ⋅ Matteo Chinazzi
Improving Generalization of Differentiable Simulator Policies with Sharpness-Aware Optimization
Severin Bochem ⋅ Eduardo Sanchez ⋅ Yves Bicker ⋅ Gabriele Fadini
Active Learning for Neural PDE Solvers
Daniel Musekamp ⋅ Marimuthu Kalimuthu ⋅ David Holzmüller ⋅ Makoto Takamoto ⋅ Mathias Niepert
Guaranteeing Conservation Laws with Projection in Physics-Informed Neural Networks
Anthony Baez ⋅ Wang Zhang ⋅ Ziwen Ma ⋅ Subhro Das ⋅ Lam Nguyen ⋅ Luca Daniel
Using Parametric PINNs for Predicting Internal and External Turbulent Flows
Shinjan Ghosh ⋅ Amit Chakraborty ⋅ Georgia Olympia Brikis ⋅ Biswadip Dey
BoostMD: Accelerated Molecular Sampling Leveraging ML Force Field Features
Lars L Schaaf ⋅ Ilyes Batatia ⋅ Jules Tilly ⋅ Tom Barrett
Beyond Closure Models: Learning Chaotic-Systems via Physics-Informed Neural Operators
Chuwei Wang ⋅ Julius Berner ⋅ Zongyi Li ⋅ Di Zhou ⋅ Jiayun (Peter) Wang ⋅ H. Jane Bae ⋅ Animashree Anandkumar
The Well: a Large-Scale Collection of Diverse Physics Simulations for Machine Learning
Ruben Ohana ⋅ Michael McCabe ⋅ Lucas Meyer ⋅ Rudy Morel ⋅ Fruzsina Agocs ⋅ Miguel Beneitez ⋅ Marsha Berger ⋅ Blakesly Burkhart ⋅ Stuart Dalziel ⋅ Drummond Fielding ⋅ Daniel Fortunato ⋅ Jared Goldberg ⋅ Keiya Hirashima ⋅ Yan-Fei Jiang ⋅ Rich Kerswell ⋅ Suryanarayana Maddu ⋅ Jonah Miller ⋅ Payel Mukhopadhyay ⋅ Stefan Nixon ⋅ Jeff Shen ⋅ Romain Watteaux ⋅ Bruno Régaldo-Saint Blancard ⋅ Liam Parker ⋅ Miles Cranmer ⋅ Shirley Ho
SepONet: Efficient Large-Scale Physics-Informed Operator Learning
Xinling Yu ⋅ Sean Hooten ⋅ Ziyue Liu ⋅ Yequan Zhao ⋅ Marco Fiorentino ⋅ Thomas Van Vaerenbergh ⋅ Zheng Zhang
SWOT-based Simulation of River Discharge with Temporal Graph Neural Networks
Kevin Osanlou ⋅ Thomas Holmes ⋅ Augusto Getirana ⋅ tristan cazenave
Learning cure kinetics of frontal polymerization PDEs using differentiable simulations
Pengfei Cai ⋅ Qibang Liu ⋅ Philippe Geubelle ⋅ Rafael Gomez-Bombarelli
Modelling variation in the forward EMG model.
Dimitrios Chalatsis ⋅ Alexander Clarke ⋅ Dario Farina
Learning Generative Interactive Environments By Trained Agent Exploration
Naser Kazemi ⋅ Nedko Savov ⋅ Danda Pani Paudel ⋅ Luc V Gool
Learnable Subset Perturbations for Understanding Transcriptional Regulatory Redundancy
Junhao Liu ⋅ Siwei Xu ⋅ Dylan Riffle ⋅ Ziheng Duan ⋅ Jing Zhang
Cost Estimation in Unit Commitment Problems Using Simulation-Based Inference
Matthias Pirlet ⋅ Adrien Bolland ⋅ Gilles Louppe ⋅ Damien Ernst