The authors present a Python library and discrete variational framework for training neural networks to solve PDEs like Stokes equations with a robust loss function tied to the true discrete error.
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Introduces Laplace-approximated Bayesian PINNs for automatic loss-weight optimization when solving PDEs such as heat, wave, and Burgers equations.
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Python library supporting Discrete Variational Formulations and training solutions with Collocation-based Robust Variational Physics Informed Neural Networks (DVF-CRVPINN)
The authors present a Python library and discrete variational framework for training neural networks to solve PDEs like Stokes equations with a robust loss function tied to the true discrete error.
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Bayesian Reasoning for Physics Informed Neural Networks
Introduces Laplace-approximated Bayesian PINNs for automatic loss-weight optimization when solving PDEs such as heat, wave, and Burgers equations.