End-to-End Differentiable Physics for Learning and Control. - Filipe de Avila Belbute-Peres; Kevin A. Smith 001; Kelsey R. Allen; Josh Tenenbaum 001; J. Zico Kolter (NeurIPS, 2018)

Combining Differentiable PDE Solvers and Graph Neural Networks for Fluid Flow Prediction. - Filipe de Avila Belbute-Peres; Thomas D. Economon; J. Zico Kolter (ICML, 2020)

HyperPINN: Learning parameterized differential equations with physics-informed hypernetworks. - Filipe de Avila Belbute-Peres; Yi-Fan Chen; Fei Sha (CoRR, 2021)

Simple initialization and parametrization of sinusoidal networks via their kernel bandwidth. - Filipe de Avila Belbute-Peres; J. Zico Kolter (ICLR, 2023)

Combining Deep Learning and Physics Models for Efficient and Robust Architectures - Filipe de Avila Belbute-Peres (2023)