Search Results for author: Filipe de Avila Belbute-Peres

Found 3 papers, 2 papers with code

HyperPINN: Learning parameterized differential equations with physics-informed hypernetworks

no code implementations NeurIPS Workshop DLDE 2021 Filipe de Avila Belbute-Peres, Yi-fan Chen, Fei Sha

Many types of physics-informed neural network models have been proposed in recent years as approaches for learning solutions to differential equations.

Combining Differentiable PDE Solvers and Graph Neural Networks for Fluid Flow Prediction

1 code implementation ICML 2020 Filipe de Avila Belbute-Peres, Thomas D. Economon, J. Zico Kolter

Solving large complex partial differential equations (PDEs), such as those that arise in computational fluid dynamics (CFD), is a computationally expensive process.

End-to-End Differentiable Physics for Learning and Control

1 code implementation NeurIPS 2018 Filipe de Avila Belbute-Peres, Kevin Smith, Kelsey Allen, Josh Tenenbaum, J. Zico Kolter

We present a differentiable physics engine that can be integrated as a module in deep neural networks for end-to-end learning.

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