Web: http://arxiv.org/abs/2209.10740

Sept. 23, 2022, 1:11 a.m. | Suresh Bishnoi, Ravinder Bhattoo, Sayan Ranu, N. M. Anoop Krishnan

cs.LG updates on arXiv.org arxiv.org

Neural networks with physics based inductive biases such as Lagrangian neural
networks (LNN), and Hamiltonian neural networks (HNN) learn the dynamics of
physical systems by encoding strong inductive biases. Alternatively, Neural
ODEs with appropriate inductive biases have also been shown to give similar
performances. However, these models, when applied to particle based systems,
are transductive in nature and hence, do not generalize to large system sizes.
In this paper, we present a graph based neural ODE, GNODE, to learn the …

arxiv biases graph inductive modeling systems

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