Pages that link to "Item:Q2057087"

The following pages link to Proof that deep artificial neural networks overcome the curse of dimensionality in the numerical approximation of Kolmogorov partial differential equations with constant diffusion and nonlinear drift coefficients (Q2057087):

Displaying 45 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)
• DNN expression rate analysis of high-dimensional PDEs: application to option pricing (Q2117328) (← links)
• A theoretical analysis of deep neural networks and parametric PDEs (Q2117329) (← links)
• The Barron space and the flow-induced function spaces for neural network models (Q2117337) (← links)
• Deep neural network approximations for solutions of PDEs based on Monte Carlo algorithms (Q2152480) (← links)
• Overcoming the curse of dimensionality in the numerical approximation of parabolic partial differential equations with gradient-dependent nonlinearities (Q2162115) (← links)
• A proof that rectified deep neural networks overcome the curse of dimensionality in the numerical approximation of semilinear heat equations (Q2216499) (← links)
• Variational Monte Carlo -- bridging concepts of machine learning and high-dimensional partial differential equations (Q2305540) (← links)
• Numerical computation of probabilities for nonlinear SDEs in high dimension using Kolmogorov equation (Q2673974) (← links)
• Active learning based sampling for high-dimensional nonlinear partial differential equations (Q2683063) (← links)
• Space-time error estimates for deep neural network approximations for differential equations (Q2683168) (← links)
• Approximation properties of residual neural networks for Kolmogorov PDEs (Q2697245) (← links)
• An overview on deep learning-based approximation methods for partial differential equations (Q2697278) (← links)
• Deep learning in high dimension: Neural network expression rates for generalized polynomial chaos expansions in UQ (Q4615657) (← links)
• Deep Splitting Method for Parabolic PDEs (Q4958922) (← links)
• Algorithms for solving high dimensional PDEs: from nonlinear Monte Carlo to machine learning (Q5019943) (← 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)
• Full error analysis for the training of deep neural networks (Q5083408) (← links)
• Deep neural network approximation for high-dimensional elliptic PDEs with boundary conditions (Q5093100) (← links)
• Unbiased Deep Solvers for Linear Parametric PDEs (Q5093244) (← links)
• Deep Neural Network Surrogates for Nonsmooth Quantities of Interest in Shape Uncertainty Quantification (Q5097855) (← links)
• A Proof that Artificial Neural Networks Overcome the Curse of Dimensionality in the Numerical Approximation of Black–Scholes Partial Differential Equations (Q5889064) (← links)
• Deep ReLU neural network approximation in Bochner spaces and applications to parametric PDEs (Q6062166) (← links)
• Exponential ReLU neural network approximation rates for point and edge singularities (Q6101269) (← links)
• XVA in a multi-currency setting with stochastic foreign exchange rates (Q6102925) (← links)
• Overall error analysis for the training of deep neural networks via stochastic gradient descent with random initialisation (Q6107984) (← links)
• An extreme learning machine-based method for computational PDEs in higher dimensions (Q6120177) (← links)
• Lower bounds for artificial neural network approximations: a proof that shallow neural networks fail to overcome the curse of dimensionality (Q6155895) (← links)
• Numerical methods for backward stochastic differential equations: a survey (Q6158181) (← links)
• Neural network approximation and estimation of classifiers with classification boundary in a Barron class (Q6165247) (← links)
• Solving Kolmogorov PDEs without the curse of dimensionality via deep learning and asymptotic expansion with Malliavin calculus (Q6176082) (← links)
• Approximation properties of residual neural networks for fractional differential equations (Q6177827) (← links)
• Mobility Estimation for Langevin Dynamics Using Control Variates (Q6178098) (← links)
• Neural network expression rates and applications of the deep parametric PDE method in counterparty credit risk (Q6549602) (← links)
• Deep neural network expressivity for optimal stopping problems (Q6565562) (← links)
• Deep learning based on randomized quasi-Monte Carlo method for solving linear Kolmogorov partial differential equation (Q6582041) (← links)
• Numerical analysis of physics-informed neural networks and related models in physics-informed machine learning (Q6598418) (← links)
• Mini-workshop: Nonlinear approximation of high-dimensional functions in scientific computing. Abstracts from the mini-workshop held October 15--20, 2023 (Q6613392) (← links)
• Rectified deep neural networks overcome the curse of dimensionality when approximating solutions of McKean-Vlasov stochastic differential equations (Q6614361) (← links)
• Recent developments in machine learning methods for stochastic control and games (Q6615618) (← links)
• The modified MSA, a gradient flow and convergence (Q6620074) (← links)
• A short note on solving partial differential equations using convolutional neural networks (Q6620254) (← links)
• Overcoming the curse of dimensionality in the numerical approximation of high-dimensional semilinear elliptic partial differential equations (Q6645961) (← links)
• Error analysis for empirical risk minimization over clipped ReLU networks in solving linear Kolmogorov partial differential equations (Q6662424) (← links)