Pages that link to "Item:Q681281"

The following pages link to Deep learning-based numerical methods for high-dimensional parabolic partial differential equations and backward stochastic differential equations (Q681281):

Displaying 50 items.

• Deep learning-based numerical methods for high-dimensional parabolic partial differential equations and backward stochastic differential equations (Q681281) (← links)
• Deep learning observables in computational fluid dynamics (Q777521) (← links)
• Weak adversarial networks for high-dimensional partial differential equations (Q777606) (← links)
• Learning constitutive relations from indirect observations using deep neural networks (Q781968) (← links)
• 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)
• Machine learning from a continuous viewpoint. I (Q829085) (← links)
• Nesting Monte Carlo for high-dimensional non-linear PDEs (Q1713854) (← links)
• The Deep Ritz Method: a deep learning-based numerical algorithm for solving variational problems (Q1744192) (← links)
• Networks for nonlinear diffusion problems in imaging (Q1988365) (← links)
• A non linear approximation method for solving high dimensional partial differential equations: application in finance (Q1996929) (← links)
• Parallel tensor methods for high-dimensional linear PDEs (Q2002269) (← links)
• DGM: a deep learning algorithm for solving partial differential equations (Q2002333) (← links)
• Computation of optimal transport and related hedging problems via penalization and neural networks (Q2020305) (← links)
• Iterative surrogate model optimization (ISMO): an active learning algorithm for PDE constrained optimization with deep neural networks (Q2021252) (← links)
• Neural networks-based backward scheme for fully nonlinear PDEs (Q2022970) (← links)
• Overcoming the curse of dimensionality in the numerical approximation of Allen-Cahn partial differential equations via truncated full-history recursive multilevel Picard approximations (Q2025321) (← links)
• An efficient numerical method for forward-backward stochastic differential equations driven by \(G\)-Brownian motion (Q2029145) (← links)
• A neural network-based policy iteration algorithm with global \(H^2\)-superlinear convergence for stochastic games on domains (Q2031059) (← links)
• Topological properties of the set of functions generated by neural networks of fixed size (Q2031060) (← links)
• Discretization and machine learning approximation of BSDEs with a constraint on the gains-process (Q2031302) (← links)
• Hierarchical deep-learning neural networks: finite elements and beyond (Q2033626) (← links)
• Deep autoencoder based energy method for the bending, vibration, and buckling analysis of Kirchhoff plates with transfer learning (Q2035195) (← links)
• A selective overview of deep learning (Q2038303) (← links)
• Computing Lyapunov functions using deep neural networks (Q2043422) (← links)
• Gradient convergence of deep learning-based numerical methods for BSDEs (Q2044106) (← links)
• Estimation of the bid-ask prices for the European discrete geometric average and arithmetic average Asian options (Q2045356) (← links)
• Numerical solution of the parametric diffusion equation by deep neural networks (Q2049099) (← links)
• Solving the Kolmogorov PDE by means of deep learning (Q2051092) (← links)
• Approximation rates for neural networks with encodable weights in smoothness spaces (Q2055067) (← links)
• Pricing equity-linked life insurance contracts with multiple risk factors by neural networks (Q2059681) (← links)
• Recurrent neural networks for stochastic control problems with delay (Q2061009) (← links)
• Reinforcement learning and stochastic optimisation (Q2072112) (← links)
• Multi-fidelity regression using artificial neural networks: efficient approximation of parameter-dependent output quantities (Q2072477) (← links)
• HiDeNN-TD: reduced-order hierarchical deep learning neural networks (Q2072507) (← links)
• Algorithms of data generation for deep learning and feedback design: a survey (Q2077720) (← links)
• High order one-step methods for backward stochastic differential equations via Itô-Taylor expansion (Q2090362) (← links)
• An application of the splitting-up method for the computation of a neural network representation for the solution for the filtering equations (Q2093308) (← links)
• Deep combinatorial optimisation for optimal stopping time problems: application to swing options pricing. (Q2094859) (← links)
• Error analysis for physics-informed neural networks (PINNs) approximating Kolmogorov PDEs (Q2095545) (← links)
• Deep learning schemes for parabolic nonlocal integro-differential equations (Q2098092) (← links)
• CAS4DL: Christoffel adaptive sampling for function approximation via deep learning (Q2098302) (← links)
• Lookback option pricing under the double Heston model using a deep learning algorithm (Q2099529) (← links)
• A non-gradient method for solving elliptic partial differential equations with deep neural networks (Q2099748) (← links)
• Convergence analysis of machine learning algorithms for the numerical solution of mean field control and games. II: The finite horizon case (Q2108885) (← links)
• Uniform convergence guarantees for the deep Ritz method for nonlinear problems (Q2110466) (← links)
• Tool path optimization of selective laser sintering processes using deep learning (Q2115582) (← links)
• Approximate value adjustments for European claims (Q2116937) (← 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)