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Found 3 papers, 3 papers with code
Stabilizing Backpropagation Through Time to Learn Complex Physics
Of all the vector fields surrounding the minima of recurrent learning setups, the gradient field with its exploding and vanishing updates appears a poor choice for optimization, offering little beyond efficient computability.
Half-Inverse Gradients for Physical Deep Learning
Recent works in deep learning have shown that integrating differentiable physics simulators into the training process can greatly improve the quality of results.
Physics-based Deep Learning
This digital book contains a practical and comprehensive introduction of everything related to deep learning in the context of physical simulations.
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