A PINN-based periodic CFD solver is shown to reach nearly the same accuracy as traditional transient-to-periodic methods but with substantially lower computational time for 2D heat diffusion and fluid flow cases.
Neural-network-based approximations for solving partial differential equations
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An adaptive anisotropic composite quadrature strategy combined with refresh-based training narrows the gap between training and reference losses in neural residual minimization for PDEs while using quadrature points more efficiently.
Side-by-side timing comparison finds BEM solves the scattering problem in ~0.01 s while PINN training takes ~100 s, but trained PINN evaluates interior points ~100x faster than BEM.
citing papers explorer
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Physics Informed Neural Network-based Computational Method for Accelerating Time-Periodic Unsteady CFD Simulations
A PINN-based periodic CFD solver is shown to reach nearly the same accuracy as traditional transient-to-periodic methods but with substantially lower computational time for 2D heat diffusion and fluid flow cases.
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Adaptive anisotropic composite quadratures for residual minimisation in neural PDE approximations
An adaptive anisotropic composite quadrature strategy combined with refresh-based training narrows the gap between training and reference losses in neural residual minimization for PDEs while using quadrature points more efficiently.
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Benchmarking Physics-Informed Neural Networks and Boundary Elements Methods for Wave Scattering
Side-by-side timing comparison finds BEM solves the scattering problem in ~0.01 s while PINN training takes ~100 s, but trained PINN evaluates interior points ~100x faster than BEM.