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Found 3 papers, 1 papers with code
Physics-informed Discovery of State Variables in Second-Order and Hamiltonian Systems
The modeling of dynamical systems is a pervasive concern for not only describing but also predicting and controlling natural phenomena and engineered systems.
Separable Hamiltonian Neural Networks
Hamiltonian neural networks (HNNs) are state-of-the-art models that regress the vector field of a dynamical system under the learning bias of Hamilton's equations.
Optimization-Informed Neural Networks
We propose optimization-informed neural networks (OINN), a deep learning approach to solve CNLPs.
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