Hard-constrained differentiable programming achieves faster convergence, better generalization, and more accurate LQR controllers than soft-constrained PINNs or data-driven NODEs on the SMIB benchmark.
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Knowledge Integration in Differentiable Models: A Comparative Study of Data-Driven, Soft-Constrained, and Hard-Constrained Paradigms for Identification and Control of the Single Machine Infinite Bus System
Hard-constrained differentiable programming achieves faster convergence, better generalization, and more accurate LQR controllers than soft-constrained PINNs or data-driven NODEs on the SMIB benchmark.