Pages that link to "Item:Q4999517"

The following pages link to Semiglobal optimal feedback stabilization of autonomous systems via deep neural network approximation (Q4999517):

Displaying 17 items.

• Computing Lyapunov functions using deep neural networks (Q2043422) (← links)
• RICAM, the Johann Radon Institute for Computational and Applied Mathematics (Q2075993) (← links)
• Imitation learning of stabilizing policies for nonlinear systems (Q2095315) (← links)
• Challenges in optimization with complex PDE-systems. Abstracts from the workshop held February 14--20, 2021 (hybrid meeting) (Q2131202) (← links)
• Learning an optimal feedback operator semiglobally stabilizing semilinear parabolic equations (Q2238960) (← links)
• State-dependent Riccati equation feedback stabilization for nonlinear PDEs (Q2692793) (← links)
• Tensor Decomposition Methods for High-dimensional Hamilton--Jacobi--Bellman Equations (Q4997370) (← links)
• An Approximation Scheme for Distributionally Robust PDE-Constrained Optimization (Q5081087) (← links)
• Data-Driven Tensor Train Gradient Cross Approximation for Hamilton–Jacobi–Bellman Equations (Q6054276) (← links)
• Optimal polynomial feedback laws for finite horizon control problems (Q6072899) (← links)
• Relaxation approach for learning neural network regularizers for a class of identification problems (Q6087358) (← links)
• Approximation of compositional functions with ReLU neural networks (Q6161370) (← links)
• Sample Size Estimates for Risk-Neutral Semilinear PDE-Constrained Optimization (Q6195313) (← links)
• A neural network approach for stochastic optimal control (Q6598497) (← links)
• A multilinear HJB-POD method for the optimal control of PDEs on a tree structure (Q6629218) (← links)
• Consistent smooth approximation of feedback laws for infinite horizon control problems with non-smooth value functions (Q6632963) (← links)
• Numerical realization of the Mortensen observer via a Hessian-augmented polynomial approximation of the value function (Q6663236) (← links)