The curvature-aware precision controller adapts between FP32 and FP64 during PINN training to match double-precision accuracy at reduced computational cost.
Self-adaptive loss balanced Physics -informed neural networks
2 Pith papers cite this work. Polarity classification is still indexing.
2
Pith papers citing it
verdicts
UNVERDICTED 2representative citing papers
NeuroSPICE uses PINNs to solve circuit DAEs via residual minimization, creating surrogate models for optimization and handling nonlinear emerging devices.
citing papers explorer
-
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.
-
Physics-Informed Neural Networks for Device and Circuit Modeling: A Case Study of NeuroSPICE
NeuroSPICE uses PINNs to solve circuit DAEs via residual minimization, creating surrogate models for optimization and handling nonlinear emerging devices.