most citedNonlinear embeddings for conserving Hamiltonians and other quantities with Neural Galerkin schemes

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math.NA2023★ 1 cited

Nonlinear embeddings for conserving Hamiltonians and other quantities with Neural Galerkin schemes

Paul Schwerdtner, Philipp Schulze, Jules Berman +1

This work focuses on the conservation of quantities such as Hamiltonians, mass, and momentum when solution fields of partial differential equations are approximated with nonlinear…

math.NA2023★ 1 cited

Lookahead data-gathering strategies for online adaptive model reduction of transport-dominated problems

Rodrigo Singh, Wayne Isaac Tan Uy, Benjamin Peherstorfer

Online adaptive model reduction efficiently reduces numerical models of transport-dominated problems by updating reduced spaces over time, which leads to nonlinear approximations o…