Systematic benchmark of PINN architectures on 1D stiff PNP system finds BRDR loss weighting competitive with NTK at lower wall-clock time.
Estimates on the generalization error of physics-informed neural networks for approximating a class of inverse problems for pdes
3 Pith papers cite this work. Polarity classification is still indexing.
3
Pith papers citing it
years
2026 3verdicts
UNVERDICTED 3representative citing papers
PhySwarm combines a multi-phase advection-diffusion-reaction density model with an equivalent microscopic motion model and a neural-physics controller trained via RL-PINN to generate and control multi-stage emergent behaviors in robot swarms.
PINNs for first-order plane-strain elastodynamics achieve higher accuracy with soft boundary enforcement over implicit geometries but require longer training than hard enforcement.
citing papers explorer
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A Systematic Benchmark of Physics-Informed Neural Network Architectures for the Stiff Poisson-Nernst-Planck System: Adaptive LossWeighting and Multi-Scale Resolution
Systematic benchmark of PINN architectures on 1D stiff PNP system finds BRDR loss weighting competitive with NTK at lower wall-clock time.
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Physics-Informed Modeling and Control of Emergent Behaviors in Robot Swarms
PhySwarm combines a multi-phase advection-diffusion-reaction density model with an equivalent microscopic motion model and a neural-physics controller trained via RL-PINN to generate and control multi-stage emergent behaviors in robot swarms.
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Exact Boundary Enforcement Along Implicit Geometries for Physics-Informed, Deep Learning Problems in Continuum Mechanics
PINNs for first-order plane-strain elastodynamics achieve higher accuracy with soft boundary enforcement over implicit geometries but require longer training than hard enforcement.