Parameterizing the temporal derivative in PINNs and reconstructing via Volterra integral yields 100-200x lower errors on advection, Burgers, and Klein-Gordon equations while proving equivalence to the original PDE.
Liu and Jorge Nocedal
2 Pith papers cite this work. Polarity classification is still indexing.
2
Pith papers citing it
representative citing papers
FANoS-v2 augments momentum optimization with feedback-controlled thermostat damping, delivering small top-1 accuracy gains over AdamW on MNIST-scale tasks while increasing wall-clock time by roughly 50%.
citing papers explorer
-
Learning on the Temporal Tangent Bundle for Physics-Informed Neural Networks
Parameterizing the temporal derivative in PINNs and reconstructing via Volterra integral yields 100-200x lower errors on advection, Burgers, and Klein-Gordon equations while proving equivalence to the original PDE.
-
FANoS-v2: Feedback-Controlled Momentum with Thermostat Damping for Lightweight Neural Optimization
FANoS-v2 augments momentum optimization with feedback-controlled thermostat damping, delivering small top-1 accuracy gains over AdamW on MNIST-scale tasks while increasing wall-clock time by roughly 50%.