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.
Karni, Hybrid schemes for euler equations, Journal of Computa- tional Physics 124 (2) (1996) 245–262
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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.