Gauss Newton method for solving variational problems of PDEs with neural network discretizaitons
DOI10.1007/s10915-024-02535-zMaRDI QIDQ6569679
Wenrui Hao, Xianlin Jin, Qingguo Hong
Publication date: 9 July 2024
Published in: Journal of Scientific Computing (Search for Journal in Brave)
convergence analysisvariational formpartial differential equationsGauss-Newton methodneural network discretization
Computational learning theory (68Q32) Artificial neural networks and deep learning (68T07) Numerical solutions to overdetermined systems, pseudoinverses (65F20) Monte Carlo methods (65C05) Numerical optimization and variational techniques (65K10) Numerical computation of solutions to systems of equations (65H10) Variational methods applied to PDEs (35A15) Stability and convergence of numerical methods for boundary value problems involving PDEs (65N12) Eigenvalues, singular values, and eigenvectors (15A18) Second-order elliptic equations (35J15) Series expansions (e.g., Taylor, Lidstone series, but not Fourier series) (41A58) Numerical methods for partial differential equations, boundary value problems (65N99) Integro-partial differential equations (35R09) PDEs on graphs and networks (ramified or polygonal spaces) (35R02)
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