A nested Fourier-MIONet surrogate predicts radiative heat transfer in multi-resolution 3D fire simulations with 2-4% error at reduced computational cost compared to direct RTE solves.
Gradient-enhanced physics-informed neural networks for forward and inverse pde problems.Computer Methods in Applied Mechanics and Engineering, 393:114823
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A multi-network PINN with NTK-based adaptive weighting jointly estimates source functions, velocity, diffusion parameters, and the solution field in advection-diffusion PDEs from noisy sparse data.
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Nested Fourier-enhanced neural operator for efficient modeling of radiation transfer in fires
A nested Fourier-MIONet surrogate predicts radiative heat transfer in multi-resolution 3D fire simulations with 2-4% error at reduced computational cost compared to direct RTE solves.
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Physics-Informed Neural Networks for Joint Source and Parameter Estimation in Advection-Diffusion Equations
A multi-network PINN with NTK-based adaptive weighting jointly estimates source functions, velocity, diffusion parameters, and the solution field in advection-diffusion PDEs from noisy sparse data.