Workshop
D3S3: Data-driven and Differentiable Simulations, Surrogates, and Solvers
Tribhuvanesh Orekondy · Arash Behboodi · Emilia Magnani · Philipp Hennig · Paris Perdikaris · Kamyar Azizzadenesheli
West Meeting Room 116, 117
Sun 15 Dec, 8:15 a.m. PST
Advancing machine learning research to aid simulations (e.g., improve accuracy, speed) in various domains (e.g., physics, graphics).
Chat is not available.
Timezone: America/Los_Angeles
Schedule
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Sun 8:15 a.m. - 8:30 a.m.
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Introduction and Opening Remarks
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Intro
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SlidesLive Video |
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Sun 8:30 a.m. - 9:00 a.m.
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Johannes Brandstetter (JKU Linz)
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Invited Talk
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SlidesLive Video |
Johannes Brandstetter 🔗 |
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Sun 9:00 a.m. - 9:30 a.m.
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Coffee Break
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Coffee Break
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Sun 9:30 a.m. - 10:00 a.m.
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Nicholas Nelsen (MIT)
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Invited Talk
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SlidesLive Video |
Nicholas Nelsen 🔗 |
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Sun 10:00 a.m. - 10:30 a.m.
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Anima Anandkumar (CalTech)
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Invited Talk
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SlidesLive Video |
Anima Anandkumar 🔗 |
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Sun 10:30 a.m. - 11:00 a.m.
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Sherry Yang (Google Deepmind)
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Invited Talk
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SlidesLive Video |
Sherry Yang 🔗 |
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Sun 11:00 a.m. - 12:00 p.m.
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Poster Session
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Poster
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Sun 12:00 p.m. - 1:00 p.m.
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Lunch Break
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Lunch Break
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Sun 1:00 p.m. - 1:30 p.m.
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Nicholas Krämer (Denmark’s TU)
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Invited Talk
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SlidesLive Video |
Nicholas Krämer 🔗 |
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Sun 1:30 p.m. - 2:00 p.m.
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Manmohan Chandraker (UC San Diego)
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Invited Talk
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SlidesLive Video |
Manmohan Chandraker 🔗 |
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Sun 2:00 p.m. - 3:00 p.m.
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Panel Discussion
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Panel Discussion
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SlidesLive Video |
Ilze Amanda Auzina · Johannes Brandstetter · Sherry Yang · Daniel Worrall · Nicholas Nelsen · Manmohan Chandraker 🔗 |
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Sun 3:00 p.m. - 3:30 p.m.
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Coffee Break
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Coffee Break
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Sun 3:30 p.m. - 4:00 p.m.
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Daniel Worrall (Google Deepmind)
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Invited Talk
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SlidesLive Video |
Daniel Worrall 🔗 |
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Sun 4:00 p.m. - 4:30 p.m.
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Ilze Amanad Auzina (U v Amsterdam)
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Invited Talk
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SlidesLive Video |
Ilze Amanda Auzina 🔗 |
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Sun 4:30 p.m. - 4:45 p.m.
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Closing Remarks
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Closing Remarks
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SlidesLive Video |
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Generative Neural Reparameterization for Differentiable PDE-Constrained Optimization ( Poster ) > link | Archis Joglekar 🔗 |
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Learning SDE Solutions with Neural Stochastic Flows ( Poster ) > link | Naoki Kiyohara · Edward Johns · Yingzhen Li 🔗 |
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Optimizing the IFMIF-DONES Particle Accelerator with Differentiable Deep Learning Surrogate Models ( Poster ) > link | Galo Gallardo · Guillermo Rodriguez Llorente · Lucas Magariños · Rodrigo Morant Navascués · Nikita Kkhvatkin Petrovsky · Roberto Gómez-Espinosa Martín 🔗 |
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VehicleSDF: A 3D generative model for constrained engineering design via surrogate modeling ( Poster ) > link | Hayata Morita · Kohei Shintani · Chenyang Yuan · Frank Permenter 🔗 |
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Spatial Shortcuts in Graph Neural Controlled Differential Equations ( Poster ) > link | Michael Detzel · Gabriel Nobis · Jackie Ma · Wojciech Samek 🔗 |
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ParaFIND: Parameter Field Inference on Non-uniform Domains using Neural Network ( Poster ) > link | Masmoudi · Xuyang Li · Nizar Lajnef · Vishnu Boddeti 🔗 |
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Projected Neural Differential Equations for Power Grid Modeling with Constraints ( Poster ) > link | Alistair White · Anna Büttner · Maximilian Gelbrecht · Niki Kilbertus · Frank Hellmann · Niklas Boers 🔗 |
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Projected Low-Rank Gradient in Diffusion-based Models for Inverse Problems ( Poster ) > link | Rayhan Zirvi · Bahareh Tolooshams · Animashree Anandkumar 🔗 |
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Neural Operators as Fast Surrogate Models for the Transmission Loss of Parameterized Sonic Crystals ( Poster ) > link | Jakob Wagner · Samuel Burbulla · Miguel de Benito Delgado · Johannes Schmid 🔗 |
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ADAGE-Diff: Two-level adaptive agent based modelling for differentiable policy design ( Poster ) > link | Benjamin Patrick Evans · Sihan Zeng · Sumitra Ganesh · Leo Ardon 🔗 |
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Wave Interpolation Neural Operator: Interpolated Prediction of Electric Fields Across Untrained Wavelengths ( Poster ) > link | Joonyuk Seo · Chanik Kang · Dongjin Seo · Haejun Chung 🔗 |
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Surrogate-based Physical Error Correction for Spectroscopy Quantification ( Poster ) > link | ruiyuan kang · Panagiotis Liatsis · Meixia Geng · Qingjie Yang 🔗 |
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Fine-Tuned MLP-Mixer Foundation Models as data-driven Numerical Surrogates? ( Poster ) > link | Imran Nasim · João Almeida 🔗 |
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Model Exploration through Marginal Likelihood Entropy Maximisation ( Poster ) > link | Daniel Jarne Ornia · Joel Dyer · Nicholas Bishop · Anisoara Calinescu · Michael Wooldridge 🔗 |
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Efficient Probabilistic Modeling of Crystallization at Mesoscopic Scale ( Poster ) > link | Pol Timmer · Koen Minartz · Vlado Menkovski 🔗 |
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Stabilizing Reinforcement Learning in Differentiable Simulation of Deformables ( Poster ) > link | Eliot Xing · Vernon Luk · Jean Oh 🔗 |
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From Function to Distribution Modeling: A PAC-Generative Approach to Offline Optimization ( Poster ) > link | Qiang Zhang · Ruida Zhou · Yang Shen · Tie Liu 🔗 |
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Gradient of Clifford Neural Networks ( Poster ) > link | Takashi Maruyama · Francesco Alesiani 🔗 |
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A neural surrogate solver for radiation transfer ( Poster ) > link | Aleksei Sorokin · Xiaoyi Lu · Yi Wang 🔗 |
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Convergence Guarantees for Neural Network-Based Hamilton–Jacobi Reachability ( Poster ) > link | William Hofgard 🔗 |
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GLEAM-AI: Neural Surrogate for Accelerated Epidemic Analytics and Forecasting ( Poster ) > link | Mohammadmehdi Zahedi · Dongxia Wu · Jessica Davis · Yian Ma · Alessandro Vespignani · Rose Yu · Matteo Chinazzi 🔗 |
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Generative Modeling and Data Augmentation for Power System Production Simulation ( Poster ) > link | Linna Xu · Yongli Zhu 🔗 |
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Improving Generalization of Differentiable Simulator Policies with Sharpness-Aware Optimization ( Poster ) > link | Severin Bochem · Eduardo Sanchez · Yves Bicker · Gabriele Fadini 🔗 |
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Active Learning for Neural PDE Solvers ( Poster ) > link | Daniel Musekamp · Marimuthu Kalimuthu · David Holzmüller · Makoto Takamoto · Mathias Niepert 🔗 |
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Guaranteeing Conservation Laws with Projection in Physics-Informed Neural Networks ( Poster ) > link | Anthony Baez · Wang Zhang · Ziwen Ma · Subhro Das · Lam Nguyen · Luca Daniel 🔗 |
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Convolutional Hierarchical Deep Learning Neural Networks-Tensor Decomposition (C-HiDeNN-TD): a scalable surrogate modeling approach for large-scale physical systems ( Poster ) > link | Jiachen Guo · Chanwook Park · Xiaoyu Xie · Zhongsheng Sang · Gregory J. Wagner · Kam Liu 🔗 |
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Using Parametric PINNs for Predicting Internal and External Turbulent Flows ( Poster ) > link | Shinjan Ghosh · Amit Chakraborty · Georgia Olympia Brikis · Biswadip Dey 🔗 |
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BoostMD: Accelerated Molecular Sampling Leveraging ML Force Field Features ( Poster ) > link | Lars L Schaaf · Ilyes Batatia · Jules Tilly · Tom Barrett 🔗 |
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When Differentiable Programming Meets Spectral PDE Solver ( Poster ) > link | Qijia Jiang 🔗 |
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Beyond Closure Models: Learning Chaotic-Systems via Physics-Informed Neural Operators ( Poster ) > link | Chuwei Wang · Julius Berner · Zongyi Li · Di Zhou · Jiayun (Peter) Wang · H. Jane Bae · Animashree Anandkumar 🔗 |
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The Well: a Large-Scale Collection of Diverse Physics Simulations for Machine Learning ( Poster ) > link |
25 presentersRuben 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 |
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SepONet: Efficient Large-Scale Physics-Informed Operator Learning ( Poster ) > link | Xinling Yu · Sean Hooten · Ziyue Liu · Yequan Zhao · Marco Fiorentino · Thomas Van Vaerenbergh · Zheng Zhang 🔗 |
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SWOT-based Simulation of River Discharge with Temporal Graph Neural Networks ( Poster ) > link | Kevin Osanlou · Thomas Holmes · Augusto Getirana · tristan cazenave 🔗 |
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Learning cure kinetics of frontal polymerization PDEs using differentiable simulations ( Poster ) > link | Pengfei Cai · Qibang Liu · Philippe Geubelle · Rafael Gomez-Bombarelli 🔗 |
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Modelling variation in the forward EMG model. ( Poster ) > link | Dimitrios Chalatsis · Alexander Clarke · Dario Farina 🔗 |
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Learning Generative Interactive Environments By Trained Agent Exploration ( Poster ) > link | Naser Kazemi · Nedko Savov · Danda Pani Paudel · Luc V Gool 🔗 |
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Learnable Subset Perturbations for Understanding Transcriptional Regulatory Redundancy ( Poster ) > link | Junhao Liu · Siwei Xu · Dylan Riffle · Ziheng Duan · Jing Zhang 🔗 |
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Cost Estimation in Unit Commitment Problems Using Simulation-Based Inference ( Poster ) > link | Matthias Pirlet · Adrien Bolland · Gilles Louppe · Damien Ernst 🔗 |