Phy2-ExposNet combines physics-informed neural estimation with transformer refinement to map electromagnetic field exposure, cutting error by ~15% and parameters by >80% versus baselines.
Pinn-fem: A hybrid approach for enforc- ing dirichlet boundary conditions in physics-informed neural networks
2 Pith papers cite this work. Polarity classification is still indexing.
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Pith papers citing it
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2026 2verdicts
UNVERDICTED 2representative citing papers
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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Phy2-ExposNet: A Physics-Informed Neural Network for EMF Exposure Mapping in Complex Urban Environments
Phy2-ExposNet combines physics-informed neural estimation with transformer refinement to map electromagnetic field exposure, cutting error by ~15% and parameters by >80% versus baselines.
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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.