Apr 23, 2022 · Abstract:Neural networks can be trained to solve partial differential equations (PDEs) by using the PDE residual as the loss function.
Apr 25, 2022 · We introduce competitive physics informed networks where two neural networks solve a partial differential equation by playing a zero-sum game.
CPINN uses an adversarial architecture to train Physics Informed Neural Networks (PINNs) against a discriminator in a zero-sum minimax game to reach higher ...
Oct 12, 2022 · Neural networks can be trained to solve partial differential equations (PDEs) by using the PDE residual as the loss function.
We experimentally show that the proposed algorithm can stabilize PINN training and yield performance competitive to the recent variants of PINNs trained with ...
TL;DR: Competitive PINNs train a discriminator that is rewarded for predicting mistakes the PINN makes, and observe relative errors on the order of ...
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Apr 28, 2022 · Physics Informed Neural Networks (PINNs) solve partial differential equations (PDEs) by representing them as neural networks.
Physics-informed neural networks (PINNs) are appealing data-driven tools for solving and inferring solutions to nonlinear partial differential equations (PDEs).
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Competitive Physics-Informed Networks · Zeng, Qi · Kothari, Yash · Bryngelson, Spencer (Georgia Institute of Technology); Schaefer, Florian (Georgia Institute ...
Feb 1, 2023 · This was a really nice and clear summary of the current state of PINNs and Scientific Machine learning. Thanks to Spencer Bryngelson also ...