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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Constraint-aware PINNs integrate epidemiological constraints into the loss function to perform forward simulation and inverse parameter estimation for SEIR reaction-diffusion systems using synthetic data from positivity-preserving NSFD schemes.
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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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Constraint-Aware Physics-Informed Neural Networks for SEIR Reaction-Diffusion Epidemic Models with Vital Dynamics
Constraint-aware PINNs integrate epidemiological constraints into the loss function to perform forward simulation and inverse parameter estimation for SEIR reaction-diffusion systems using synthetic data from positivity-preserving NSFD schemes.