The curvature-aware precision controller adapts between FP32 and FP64 during PINN training to match double-precision accuracy at reduced computational cost.
DAS-PINNs:Adeepadaptivesamplingmethodforsolvinghigh-dimensionalpartialdifferentialequations
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DAS-PINNs uses normalizing flows to adaptively sample collocation points based on PDE residuals in unified spacetime domains for high-dimensional time-dependent PDEs.
citing papers explorer
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Curvature-aware dynamic precision approach for physics-informed neural networks
The curvature-aware precision controller adapts between FP32 and FP64 during PINN training to match double-precision accuracy at reduced computational cost.
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DAS-PINNs for high-dimensional partial differential equations: extending deep adaptive sampling to spacetime domains
DAS-PINNs uses normalizing flows to adaptively sample collocation points based on PDE residuals in unified spacetime domains for high-dimensional time-dependent PDEs.