DC-PINNs embed derivative constraints into PINN optimization using a minimum principle and adaptive balancing, reducing violations and improving fidelity on heat, finance, and fluid benchmarks.
The metrics include RMSE of (u, v, p), divergence error, violation rates of in- equality constraints, and running time efficiency
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Physics-Informed Neural Networks for Solving Derivative-Constrained PDEs
DC-PINNs embed derivative constraints into PINN optimization using a minimum principle and adaptive balancing, reducing violations and improving fidelity on heat, finance, and fluid benchmarks.