RAYEN enforces hard convex constraints (linear, quadratic, SOC, LMI) on neural networks with negligible overhead while guaranteeing satisfaction at all times.
Physics- informed neural networks: A deep learning framework for solving forward and inverse problems involving nonlinear partial differential equations
2 Pith papers cite this work. Polarity classification is still indexing.
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A PINN approach learns nearly maximal Lyapunov functions via Zubov's equation and verifies stability with SMT solvers.
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RAYEN: Imposition of Hard Convex Constraints on Neural Networks
RAYEN enforces hard convex constraints (linear, quadratic, SOC, LMI) on neural networks with negligible overhead while guaranteeing satisfaction at all times.
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Towards Learning and Verifying Maximal Neural Lyapunov Functions
A PINN approach learns nearly maximal Lyapunov functions via Zubov's equation and verifies stability with SMT solvers.