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Semi-supervised Learning of Partial Differential Operators and Dynamical Flows. (arXiv:2207.14366v1 [cs.LG])
cs.LG updates on arXiv.org arxiv.org
The evolution of dynamical systems is generically governed by nonlinear
partial differential equations (PDEs), whose solution, in a simulation
framework, requires vast amounts of computational resources. In this work, we
present a novel method that combines a hyper-network solver with a Fourier
Neural Operator architecture. Our method treats time and space separately. As a
result, it successfully propagates initial conditions in continuous time steps
by employing the general composition properties of the partial differential
operators. Following previous work, supervision is …
arxiv learning lg operators semi-supervised semi-supervised learning supervised learning