Pages that link to "Item:Q4967451"

The following pages link to Solving high-dimensional partial differential equations using deep learning (Q4967451):

Displaying 50 items.

• Deep neural network framework based on backward stochastic differential equations for pricing and hedging American options in high dimensions (Q5014169) (← links)
• Nested Monte Carlo simulation in financial reporting: a review and a new hybrid approach (Q5014496) (← links)
• Learning and meta-learning of stochastic advection–diffusion–reaction systems from sparse measurements (Q5014838) (← links)
• A multi-level procedure for enhancing accuracy of machine learning algorithms (Q5014840) (← links)
• Solving high-dimensional optimal stopping problems using deep learning (Q5014845) (← links)
• Higher-Order Quasi-Monte Carlo Training of Deep Neural Networks (Q5015302) (← 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)
• Optimization with learning-informed differential equation constraints and its applications (Q5024338) (← links)
• Optimizing Noisy Complex Systems Liable to Failure (Q5024349) (← links)
• Analysis of the Generalization Error: Empirical Risk Minimization over Deep Artificial Neural Networks Overcomes the Curse of Dimensionality in the Numerical Approximation of Black--Scholes Partial Differential Equations (Q5037569) (← links)
• On a multilevel Levenberg–Marquardt method for the training of artificial neural networks and its application to the solution of partial differential equations (Q5038185) (← links)
• Deep Adaptive Basis Galerkin Method for High-Dimensional Evolution Equations With Oscillatory Solutions (Q5038412) (← links)
• Newton Method for Stochastic Control Problems (Q5039281) (← links)
• (Q5043153) (← links)
• PFNN-2: A Domain Decomposed Penalty-Free Neural Network Method for Solving Partial Differential Equations (Q5045670) (← links)
• Generalization Error Analysis of Neural Networks with Gradient Based Regularization (Q5045671) (← links)
• Learning a functional control for high-frequency finance (Q5051970) (← links)
• Error Estimates for a Tree Structure Algorithm Solving Finite Horizon Control Problems (Q5056666) (← 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)
• Pricing Options under Rough Volatility with Backward SPDEs (Q5065084) (← links)
• A Neural Network Approach to Sampling Based Learning Control for Quantum System with Uncertainty (Q5065154) (← links)
• An Augmented Lagrangian Deep Learning Method for Variational Problems with Essential Boundary Conditions (Q5065200) (← links)
• (Q5066183) (← links)
• Probabilistic Approach to Mean Field Games and Mean Field Type Control Problems with Multiple Populations (Q5074077) (← links)
• A Deep Learning Method for Elliptic Hemivariational Inequalities (Q5074898) (← links)
• Approximations with deep neural networks in Sobolev time-space (Q5075578) (← links)
• Deep Unfitted Nitsche Method for Elliptic Interface Problems (Q5077697) (← links)
• A Rate of Convergence of Physics Informed Neural Networks for the Linear Second Order Elliptic PDEs (Q5077701) (← links)
• A Symplectic Based Neural Network Algorithm for Quantum Controls under Uncertainty (Q5077710) (← links)
• Physics Informed Neural Networks (PINNs) For Approximating Nonlinear Dispersive PDEs (Q5079535) (← links)
• State-Dependent Temperature Control for Langevin Diffusions (Q5080490) (← links)
• An unsupervised deep learning approach to solving partial integro-differential equations (Q5092661) (← links)
• Unbiased Deep Solvers for Linear Parametric PDEs (Q5093244) (← links)
• Active Neuron Least Squares: A Training Method for Multivariate Rectified Neural Networks (Q5095494) (← links)
• On a Neural Network to Extract Implied Information from American Options (Q5103918) (← links)
• (Q5104591) (← links)
• Solving Time Dependent Fokker-Planck Equations via Temporal Normalizing Flow (Q5106295) (← links)
• Deep ReLU networks and high-order finite element methods (Q5132226) (← links)
• Error bounds for approximations with deep ReLU neural networks in Ws,p norms (Q5132228) (← links)
• Numerical solution of inverse problems by weak adversarial networks (Q5132263) (← links)
• Optimizing a portfolio of mean-reverting assets with transaction costs via a feedforward neural network (Q5139230) (← links)
• Accelerated share repurchase and other buyback programs: what neural networks can bring (Q5139239) (← links)
• Structure-Preserving Method for Reconstructing Unknown Hamiltonian Systems From Trajectory Data (Q5147965) (← links)
• DeepXDE: A Deep Learning Library for Solving Differential Equations (Q5150214) (← links)
• Discovery of Dynamics Using Linear Multistep Methods (Q5151929) (← links)
• Approximate Optimal Controls via Instanton Expansion for Low Temperature Free Energy Computation (Q5157688) (← links)
• Bayesian differential programming for robust systems identification under uncertainty (Q5161165) (← links)