Deep mixed residual method for solving PDE-constrained optimization problems

Computational learning theory (68Q32) Artificial neural networks and deep learning (68T07) Numerical methods for partial differential equations, boundary value problems (65N99) PDE constrained optimization (numerical aspects) (49M41)

Cites Work

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Weak adversarial networks for high-dimensional partial differential equations
Controlling oscillations in high-order discontinuous Galerkin schemes using artificial viscosity tuned by neural networks
Heat transfer optimization in corrugated wall channels
The Deep Ritz Method: a deep learning-based numerical algorithm for solving variational problems
DGM: a deep learning algorithm for solving partial differential equations
Iterative surrogate model optimization (ISMO): an active learning algorithm for PDE constrained optimization with deep neural networks
Optimal control of PDEs using physics-informed neural networks
MIM: a deep mixed residual method for solving high-order partial differential equations
DeLISA: deep learning based iteration scheme approximation for solving PDEs
Surrogate modeling for fluid flows based on physics-constrained deep learning without simulation data
Solving many-electron Schrödinger equation using deep neural networks
Physics-informed neural networks: a deep learning framework for solving forward and inverse problems involving nonlinear partial differential equations
A Priori Error Estimates of <scp>C</scp> rank–Nicolson Finite Volume Element Method for a Hyperbolic Optimal Control Problem
Numerical PDE-Constrained Optimization
Algorithms for PDE-constrained optimization
Optimization with PDE Constraints
Deep learning of vortex-induced vibrations
Solving high-dimensional partial differential equations using deep learning
Hidden fluid mechanics: Learning velocity and pressure fields from flow visualizations
Improved Discrete Boundary Type Shape Gradients for PDE-constrained Shape Optimization
Real-Time PDE-Constrained Optimization
Neural network approximation
An extended physics informed neural network for preliminary analysis of parametric optimal control problems
Deep neural networks learning forward and inverse problems of two-dimensional nonlinear wave equations with rational solitons
Failure-Informed Adaptive Sampling for PINNs
A priori error estimate of deep mixed residual method for elliptic PDEs
The use of physics-informed neural network approach to image restoration via nonlinear PDE tools

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