Physics-constrained deep learning for high-dimensional surrogate modeling and uncertainty quantification without labeled data
A project on how to incorporate physics constraints into deep learning architectures for downscaling or other super--resolution tasks.
Physics-constrained auto-regressive convolutional neural networks for dynamical PDEs
PECANNs: Physics and Equality Constrained Artificial Neural Networks
A project on how to incorporate physics constraints into deep learning architectures for downscaling or other super--resolution tasks.
AI4Engineering: Machine learning surrogates for universal microstructure-property forecasting with physical guarantees
Discover physical correction laws from anomalous data in JAX.
PyTorch training and validation for the IONIS (Ionospheric Neural Inference System) propagation model.
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