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Poster
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Tue 9:00
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A composable machine-learning approach for steady-state simulations on high-resolution grids
Rishikesh Ranade · Chris Hill · Lalit Ghule · Jay Pathak
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Workshop
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Learning Ordinary Differential Equations with the Line Integral Loss Function
Albert Johannessen
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Workshop
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Neuro-Symbolic Partial Differential Equation Solver
Pouria Akbari Mistani · Samira Pakravan · Rajesh Ilango · Sanjay Choudhry · Frederic Gibou
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Poster
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Wed 14:00
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Neural Differential Equations for Learning to Program Neural Nets Through Continuous Learning Rules
Kazuki Irie · Francesco Faccio · Jürgen Schmidhuber
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Poster
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Wed 14:00
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Learning to Accelerate Partial Differential Equations via Latent Global Evolution
Tailin Wu · Takashi Maruyama · Jure Leskovec
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Poster
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Wed 14:00
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Constraining Gaussian Processes to Systems of Linear Ordinary Differential Equations
Andreas Besginow · Markus Lange-Hegermann
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Workshop
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Learning and Shaping Manifold Attractors for Computation in Gated Neural ODEs
Timothy Kim · Tankut Can · Kamesh Krishnamurthy
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Workshop
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Efficient Robustness Verification of Neural Ordinary Differential Equations
Mustafa Zeqiri · Mark Müller · Marc Fischer · Martin Vechev
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Poster
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Wed 9:00
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Learning Modular Simulations for Homogeneous Systems
Jayesh Gupta · Sai Vemprala · Ashish Kapoor
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Poster
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Tue 14:00
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Fast Mixing of Stochastic Gradient Descent with Normalization and Weight Decay
Zhiyuan Li · Tianhao Wang · Dingli Yu
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Poster
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Wed 14:00
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Score-Based Generative Models Detect Manifolds
Jakiw Pidstrigach
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Poster
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Thu 9:00
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PDEBench: An Extensive Benchmark for Scientific Machine Learning
Makoto Takamoto · Timothy Praditia · Raphael Leiteritz · Daniel MacKinlay · Francesco Alesiani · Dirk Pflüger · Mathias Niepert
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