Direct Poisson neural networks: learning non-symplectic mechanical systems
DOI10.1088/1751-8121/ad0803arXiv2305.05540OpenAlexW4388016218MaRDI QIDQ6148231
Martin Šípka, Miroslav Grmela, Oğul Esen, Michal Pavelka
Publication date: 11 January 2024
Published in: Journal of Physics A: Mathematical and Theoretical (Search for Journal in Brave)
Full work available at URL: https://arxiv.org/abs/2305.05540
Artificial neural networks and deep learning (68T07) Learning and adaptive systems in artificial intelligence (68T05) Computational methods for problems pertaining to mechanics of particles and systems (70-08) Differential geometric methods (tensors, connections, symplectic, Poisson, contact, Riemannian, nonholonomic, etc.) for problems in mechanics (70G45) Canonical and symplectic transformations for problems in Hamiltonian and Lagrangian mechanics (70H15)
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