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.
Computer Methods in Applied Mechanics and Engineering358(2020)
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A review assessing PINN advances for forward modeling, inverse design, and equation discovery across multi-physics domains.
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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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Beyond Data-Driven: How Physics-Informed Neural Networks are Reshaping Multi-Physics Design and Discovery
A review assessing PINN advances for forward modeling, inverse design, and equation discovery across multi-physics domains.