12 citations · 23 across the 3 of their papers we have counts for
3 papers
Simple initialization and parametrization of sinusoidal networks via their kernel bandwidth
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. Despite their promise, particularly for learning…
HyperPINN: Learning parameterized differential equations with physics-informed hypernetworks
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. When a particular task requi…
Combining Differentiable PDE Solvers and Graph Neural Networks for Fluid Flow Prediction
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. This has motiva…