Pages that link to "Item:Q2683498"
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The following pages link to Neural network architectures using min-plus algebra for solving certain high-dimensional optimal control problems and Hamilton-Jacobi PDEs (Q2683498):
Displaying 11 items.
- Overcoming the curse of dimensionality for some Hamilton-Jacobi partial differential equations via neural network architectures (Q783094) (← links)
- Solving high-dimensional Hamilton-Jacobi-Bellman PDEs using neural networks: perspectives from the theory of controlled diffusions and measures on path space (Q825596) (← links)
- Tensor decomposition and high-performance computing for solving high-dimensional stochastic control system numerically (Q2121191) (← links)
- SympOCnet: Solving Optimal Control Problems with Applications to High-Dimensional Multiagent Path Planning Problems (Q5058288) (← links)
- Value-Gradient Based Formulation of Optimal Control Problem and Machine Learning Algorithm (Q6040292) (← links)
- Data-Driven Tensor Train Gradient Cross Approximation for Hamilton–Jacobi–Bellman Equations (Q6054276) (← links)
- Discrete-time nonlinear feedback linearization via physics-informed machine learning (Q6078485) (← links)
- Relaxation approach for learning neural network regularizers for a class of identification problems (Q6087358) (← links)
- Neural network architectures using min-plus algebra for solving certain high dimensional optimal control problems and Hamilton-Jacobi PDEs (Q6367093) (← links)
- Lax-Oleinik-type formulas and efficient algorithms for certain high-dimensional optimal control problems (Q6575313) (← links)
- A multilinear HJB-POD method for the optimal control of PDEs on a tree structure (Q6629218) (← links)
