A non-overlapping Schwarz hybrid FE-NO framework with Point-DeepONet enables efficient, geometry-flexible simulations of solid mechanics by reducing interface iterations and enforcing mechanical consistency through analytical strain-stress derivation.
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Presents DHPO and a pretrained DeepONet inverse modeling framework that discovers unknown PDE terms and infers parameters across equation families with O(10^-2) solution and O(10^-3) parameter errors on benchmarks.
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A Non-Overlapping Schwarz Hybrid Finite Element-Neural Operator Framework for Solid Mechanics on Irregular Domains
A non-overlapping Schwarz hybrid FE-NO framework with Point-DeepONet enables efficient, geometry-flexible simulations of solid mechanics by reducing interface iterations and enforcing mechanical consistency through analytical strain-stress derivation.
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Learning Hidden Physics and System Parameters with Deep Operator Networks
Presents DHPO and a pretrained DeepONet inverse modeling framework that discovers unknown PDE terms and infers parameters across equation families with O(10^-2) solution and O(10^-3) parameter errors on benchmarks.