Pages that link to "Item:Q4997364"
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The following pages link to Adaptive Deep Learning for High-Dimensional Hamilton--Jacobi--Bellman Equations (Q4997364):
Displaying 32 items.
- Solving high-dimensional Hamilton-Jacobi-Bellman PDEs using neural networks: perspectives from the theory of controlled diffusions and measures on path space (Q825596) (← links)
- Algorithms of data generation for deep learning and feedback design: a survey (Q2077720) (← links)
- SelectNet: self-paced learning for high-dimensional partial differential equations (Q2131038) (← links)
- Random features for high-dimensional nonlocal mean-field games (Q2137934) (← links)
- Newton's method, Bellman recursion and differential dynamic programming for unconstrained nonlinear dynamic games (Q2150657) (← links)
- Convergence of deep fictitious play for stochastic differential games (Q2170300) (← links)
- Neural network architectures using min-plus algebra for solving certain high-dimensional optimal control problems and Hamilton-Jacobi PDEs (Q2683498) (← links)
- State-dependent Riccati equation feedback stabilization for nonlinear PDEs (Q2692793) (← links)
- Mini-workshop: Analysis of data-driven optimal control. Abstracts from the mini-workshop held May 9--15, 2021 (hybrid meeting) (Q2693004) (← links)
- Physics-informed neural networks based on adaptive weighted loss functions for Hamilton-Jacobi equations (Q2694112) (← links)
- A data-driven approximate solution to the model-free HJB equation (Q3176468) (← links)
- Polynomial Approximation of High-Dimensional Hamilton--Jacobi--Bellman Equations and Applications to Feedback Control of Semilinear Parabolic PDEs (Q4607635) (← links)
- Semiglobal optimal feedback stabilization of autonomous systems via deep neural network approximation (Q4999517) (← links)
- Algorithms for solving high dimensional PDEs: from nonlinear Monte Carlo to machine learning (Q5019943) (← links)
- Actor-Critic Method for High Dimensional Static Hamilton--Jacobi--Bellman Partial Differential Equations based on Neural Networks (Q5021407) (← links)
- (Q5053195) (← links)
- SympOCnet: Solving Optimal Control Problems with Applications to High-Dimensional Multiagent Path Planning Problems (Q5058288) (← links)
- Feedforward Neural Networks and Compositional Functions with Applications to Dynamical Systems (Q5065061) (← links)
- A Deep Learning Method for Elliptic Hemivariational Inequalities (Q5074898) (← links)
- Approximating Optimal feedback Controllers of Finite Horizon Control Problems Using Hierarchical Tensor Formats (Q5084512) (← 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)
- Optimal polynomial feedback laws for finite horizon control problems (Q6072899) (← links)
- A mathematical perspective of machine learning (Q6118171) (← links)
- An extreme learning machine-based method for computational PDEs in higher dimensions (Q6120177) (← links)
- Variable separated physics-informed neural networks based on adaptive weighted loss functions for blood flow model (Q6144182) (← links)
- Approximation of compositional functions with ReLU neural networks (Q6161370) (← links)
- Adaptive deep neural networks for solving corner singular problems (Q6545698) (← links)
- An optimal control method to compute the most likely transition path for stochastic dynamical systems with jumps (Q6563609) (← links)
- Lax-Oleinik-type formulas and efficient algorithms for certain high-dimensional optimal control problems (Q6575313) (← 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)
