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Generalization Bounds for Physics-Informed Neural Networks for the Incompressible Navier-Stokes Equations FW08HEAPKW

URIm2://res/FW08HEAPKW
Kindpreprint
CreatorsSebastien Andre-Sloan, Dibyakanti Kumar, Alejandro F Frangi, Anirbit Mukherjee
Date2026-03-24
Subjectsarxiv cs.LG, arxiv cs.NA, arxiv math.AP, arxiv math.NA
Scopeborder

Expressions

e1: version, preprint

Date2026-03-24
IdsarXiv 2603.23072

Items in this resource

The mentions of the inventory that name this resource. Each name links to the card of its inventory row.

ItemKindWhereSource
20XW91W0FVstatementlemma 6arxiv
\bf Neural Network for $(d+1)$-Navier-Stokesdefinitiondefinition 2arxiv
\bf Loss Function for $(d+1)$-Navier-Stokesdefinitiondefinition 5arxiv
\bf Collocation Points for $(d+1)$-Navier-Stokesdefinitiondefinition 3arxiv
\bf Class of Weights for $(d+1)$-Navier-Stokesdefinitiondefinition 6arxiv
J1ZV1CZYP8statementlemma 4arxiv
\bf Rademacher Based Upper-bound for $(d+1)$-Navier-Stokesstatementtheorem 1arxiv
N2Z53672S0statementlemma 2arxiv
P5ZDZHZHTDstatementlemma 5arxiv
RX61VHB79Estatementlemma 3arxiv
S33EZ911FDstatementtheoremarxiv
\bf Empirical Risk for $(d+1)$-Navier-Stokesdefinitiondefinition 4arxiv
\bf Huber Lossdefinitiondefinition 1arxiv
XHXDHK4T68statementlemma 7arxiv

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