PVD-ONet combines multi-network DeepONet modules with Prandtl and Van Dyke matching conditions to map initial data to solution operators for families of singularly perturbed boundary-layer problems and to infer scaling exponents from sparse observations.
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A robust collocation-based PINN framework with variational stability simulates time-dependent pollution propagation, demonstrating that thermal inversions significantly increase particulate matter concentrations from snowmobile traffic in Longyearbyen.
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PVD-ONet: A Multi-scale Neural Operator Method for Singularly Perturbed Boundary Layer Problems
PVD-ONet combines multi-network DeepONet modules with Prandtl and Van Dyke matching conditions to map initial data to solution operators for families of singularly perturbed boundary-layer problems and to infer scaling exponents from sparse observations.
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Collocation-based Robust Physics Informed Neural Networks for time-dependent simulations of pollution propagation under thermal inversion conditions on Spitsbergen
A robust collocation-based PINN framework with variational stability simulates time-dependent pollution propagation, demonstrating that thermal inversions significantly increase particulate matter concentrations from snowmobile traffic in Longyearbyen.