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Symplectic Neural Networks for Learning Non-Separable Hamiltonians
Harsh Choudhary, Vyacheslav Kungurtsev, Chandan Gupta +2
Hamiltonian Neural Networks (HNNs) integrate physical priors into neural models by learning a system's Hamiltonian, improving generalization and sample efficiency. Identifying the…
Binarizing Physics-Inspired GNNs for Combinatorial Optimization
Martin Krutský, Gustav Šír, Vyacheslav Kungurtsev +1
Physics-inspired graph neural networks (PI-GNNs) have been utilized as an efficient unsupervised framework for relaxing combinatorial optimization problems encoded through a specif…
ExMAG: Learning of Maximally Ancestral Graphs
Petr Ryšavý, Pavel Rytíř, Xiaoyu He +2
In mixed graphs, there are both directed and bidirected edges. An extension of acyclicity to this mixed-graph setting is known as maximally ancestral graphs. This extension is of c…
Learning Generalized Hamiltonians using fully Symplectic Mappings
Harsh Choudhary, Chandan Gupta, Vyacheslav Kungurtsev +2
Many important physical systems can be described as the evolution of a Hamiltonian system, which has the important property of being conservative, that is, energy is conserved thro…