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cs.LG2026
PL-KKT-hPINN: Enforcing Nonlinear Equality Constraints on Neural Networks via Piecewise-Linear Projection
Fateme Mohammad Mohammadi, Hector Budman, Joshua L. Pulsipher
While physics-informed neural networks (PINNs) have shown strong potential for process modeling, physical equations are only enforced as soft constraints during training, and thus,…
eess.SY2026
GPU-Accelerated Direct Transcription-Based Nonlinear Model Predictive Control
Evelyn Gondosiswanto, Joshua L. Pulsipher
In this paper, we present a GPU-accelerated framework for nonlinear model predictive control (NMPC) based on direct transcription and second-order interior-point methods. Many real…