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.
Physics-informed neural networks for inverse problems in nano-optics and metamaterials,
3 Pith papers cite this work. Polarity classification is still indexing.
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2026 3verdicts
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The authors present a Python library and discrete variational framework for training neural networks to solve PDEs like Stokes equations with a robust loss function tied to the true discrete error.
A review assessing PINN advances for forward modeling, inverse design, and equation discovery across multi-physics domains.
citing papers explorer
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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.
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Python library supporting Discrete Variational Formulations and training solutions with Collocation-based Robust Variational Physics Informed Neural Networks (DVF-CRVPINN)
The authors present a Python library and discrete variational framework for training neural networks to solve PDEs like Stokes equations with a robust loss function tied to the true discrete error.
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Beyond Data-Driven: How Physics-Informed Neural Networks are Reshaping Multi-Physics Design and Discovery
A review assessing PINN advances for forward modeling, inverse design, and equation discovery across multi-physics domains.