GEN is a neural network that solves PDEs by constructing explicit function approximations from basis functions based on prior PDE knowledge, yielding more robust and extensible solutions than standard PINNs.
Experience report of physics-informed neural networks in fluid simula- tions: pitfalls and frustration
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Hybrid quantum-classical physics-informed neural networks reach accurate solutions to nonlinear PDEs in substantially fewer training epochs than purely classical networks, with larger gains on complex problems.
citing papers explorer
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General Explicit Network (GEN): A novel deep learning architecture for solving partial differential equations
GEN is a neural network that solves PDEs by constructing explicit function approximations from basis functions based on prior PDE knowledge, yielding more robust and extensible solutions than standard PINNs.
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Quantum-Enhanced Convergence of Physics-Informed Neural Networks
Hybrid quantum-classical physics-informed neural networks reach accurate solutions to nonlinear PDEs in substantially fewer training epochs than purely classical networks, with larger gains on complex problems.