ModLaNets: Learning Generalisable Dynamics via Modularity and Physical Inductive Bias. (arXiv:2206.12325v1 [cs.LG])

Deep learning models are able to approximate one specific dynamical system
but struggle at learning generalisable dynamics, where dynamical systems obey
the same laws of physics but contain different numbers of elements (e.g.,
double- and triple-pendulum systems). To relieve this issue, we proposed the
Modular Lagrangian Network (ModLaNet), a structural neural network framework
with modularity and physical inductive bias. This framework models the energy
of each element using modularity and then construct the target dynamical system
via Lagrangian mechanics. Modularity …

arxiv bias dynamics inductive learning lg