Pages that link to "Item:Q825596"
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The following pages link to Solving high-dimensional Hamilton-Jacobi-Bellman PDEs using neural networks: perspectives from the theory of controlled diffusions and measures on path space (Q825596):
Displaying 27 items.
- Bayesian learning via neural Schrödinger-Föllmer flows (Q2104005) (← links)
- Error bounds for model reduction of feedback-controlled linear stochastic dynamics on Hilbert spaces (Q2137753) (← links)
- Extensions of the deep Galerkin method (Q2148058) (← 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)
- An overview on deep learning-based approximation methods for partial differential equations (Q2697278) (← links)
- Adaptive Deep Learning for High-Dimensional Hamilton--Jacobi--Bellman Equations (Q4997364) (← links)
- Algorithms for solving high dimensional PDEs: from nonlinear Monte Carlo to machine learning (Q5019943) (← links)
- Approximation Error Analysis of Some Deep Backward Schemes for Nonlinear PDEs (Q5021399) (← links)
- Actor-Critic Method for High Dimensional Static Hamilton--Jacobi--Bellman Partial Differential Equations based on Neural Networks (Q5021407) (← links)
- Approximating Optimal feedback Controllers of Finite Horizon Control Problems Using Hierarchical Tensor Formats (Q5084512) (← links)
- Neural Parametric Fokker--Planck Equation (Q5087103) (← links)
- Approximative Policy Iteration for Exit Time Feedback Control Problems Driven by Stochastic Differential Equations using Tensor Train Format (Q5865245) (← links)
- Overcoming the timescale barrier in molecular dynamics: Transfer operators, variational principles and machine learning (Q6047503) (← links)
- A Neural Network Approach to High-Dimensional Optimal Switching Problems with Jumps in Energy Markets (Q6070671) (← links)
- Importance sampling for the empirical measure of weakly interacting diffusions (Q6142541) (← links)
- Numerical methods for backward stochastic differential equations: a survey (Q6158181) (← links)
- Learning-based importance sampling via stochastic optimal control for stochastic reaction networks (Q6172912) (← links)
- Mobility Estimation for Langevin Dynamics Using Control Variates (Q6178098) (← links)
- Learning the random variables in Monte Carlo simulations with stochastic gradient descent: Machine learning for parametric PDEs and financial derivative pricing (Q6178392) (← links)
- Stein variational gradient descent: many-particle and long-time asymptotics (Q6194470) (← links)
- Neural Control of Parametric Solutions for High-Dimensional Evolution PDEs (Q6194975) (← links)
- Numerical solutions of sea turtle population dynamics model by using restarting strategy of PINN-Adam (Q6568889) (← links)
- Connecting stochastic optimal control and reinforcement learning (Q6597633) (← links)
- Hamilton-Jacobi equations and mathematical morphology in pseudo-Riemannian manifolds (Q6610746) (← links)
- Double-loop importance sampling for McKean-Vlasov stochastic differential equation (Q6643237) (← links)
- Approximation of optimal feedback controls for stochastic reaction-diffusion equations (Q6664371) (← links)
