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.
2 code implementations • 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.
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.
no code implementations • 26 Nov 2022 • Filipe de Avila Belbute-Peres, J. Zico Kolter
Neural networks with sinusoidal activations have been proposed as an alternative to networks with traditional activation functions.
