A conditional latent diffusion model generates physics-informed neural network initializations that speed up seismic wavefield PINN training and improve accuracy.
Finite-element simulation of seismic ground motion with a voxel mesh
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DiffPINN: Generative diffusion-initialized physics-informed neural networks for accelerating seismic wavefield representation
A conditional latent diffusion model generates physics-informed neural network initializations that speed up seismic wavefield PINN training and improve accuracy.