Physics-Informed Neural networks for Advanced modeling
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Updated
Aug 1, 2025 - Python
Physics-Informed Neural networks for Advanced modeling
Physics-constrained auto-regressive convolutional neural networks for dynamical PDEs
Research project conducted at Pacific Northwest National Laboratory, exploring the use of physics-informed autoencoders to predict fluid flow dynamics
TensorFlow 2.0 implementation of Yibo Yang, Paris Perdikaris’s adversarial Uncertainty Quantification in Physics Informed Neural Networks (UQPINNs).
Bart.dIAs is the WebGRIPP Project's Coding Assistant. Currently it is a simple (parallel) coding assistant.
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