UM-PINN reinterprets PINN training as multi-task learning with homoscedastic uncertainty and a gradient-based spatial mask to improve shock resolution in 1D and 2D hyperbolic problems.
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2 Pith papers cite this work. Polarity classification is still indexing.
2
Pith papers citing it
years
2026 2verdicts
UNVERDICTED 2representative citing papers
PINNs and DeepONets solve Newtonian plane Couette flow with dynamic wall slip; DeepONet achieves 0.36% mean relative error on unseen cases and 540X speedup over numerical methods.
citing papers explorer
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Spatio-Temporal Uncertainty-Modulated Physics-Informed Neural Networks for Solving Hyperbolic Conservation Laws with Strong Shocks
UM-PINN reinterprets PINN training as multi-task learning with homoscedastic uncertainty and a gradient-based spatial mask to improve shock resolution in 1D and 2D hyperbolic problems.
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Solution of the Newtonian plane Couette flow with dynamic wall slip using machine-learning methods
PINNs and DeepONets solve Newtonian plane Couette flow with dynamic wall slip; DeepONet achieves 0.36% mean relative error on unseen cases and 540X speedup over numerical methods.