Pages that link to "Item:Q4967451"

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

Displaying 50 items.

• Discrete-Time Approximation of Stochastic Optimal Control with Partial Observation (Q6148450) (← links)
• Convergence Analysis of a Quasi-Monte CarloBased Deep Learning Algorithm for Solving Partial Differential Equations (Q6151262) (← links)
• MC-Nonlocal-PINNs: Handling Nonlocal Operators in PINNs Via Monte Carlo Sampling (Q6151271) (← links)
• NeuralUQ: A Comprehensive Library for Uncertainty Quantification in Neural Differential Equations and Operators (Q6154538) (← links)
• Less is more: a new machine-learning methodology for spatiotemporal systems (Q6156520) (← links)
• Boundary-safe PINNs extension: application to non-linear parabolic PDEs in counterparty credit risk (Q6157931) (← links)
• Numerical methods for backward stochastic differential equations: a survey (Q6158181) (← links)
• Meshless methods for American option pricing through physics-informed neural networks (Q6158655) (← links)
• Numerical computation of partial differential equations by hidden-layer concatenated extreme learning machine (Q6159015) (← links)
• Deep xVA Solver: A Neural Network–Based Counterparty Credit Risk Management Framework (Q6159074) (← links)
• Deep Curve-Dependent PDEs for Affine Rough Volatility (Q6159075) (← links)
• An introduction to kernel and operator learning methods for homogenization by self-consistent clustering analysis (Q6159333) (← links)
• Approximation of compositional functions with ReLU neural networks (Q6161370) (← links)
• Neural network approximation and estimation of classifiers with classification boundary in a Barron class (Q6165247) (← links)
• Improved training of physics-informed neural networks for parabolic differential equations with sharply perturbed initial conditions (Q6171154) (← links)
• Deep Weak Approximation of SDEs: A Spatial Approximation Scheme for Solving Kolmogorov Equations (Q6173002) (← links)
• Adaptive Learning Rate Residual Network Based on Physics-Informed for Solving Partial Differential Equations (Q6173072) (← links)
• RelaxNet: a structure-preserving neural network to approximate the Boltzmann collision operator (Q6173349) (← links)
• Mobility Estimation for Langevin Dynamics Using Control Variates (Q6178098) (← links)
• A Neural Network Approach for Homogenization of Multiscale Problems (Q6178099) (← links)
• Learning the random variables in Monte Carlo simulations with stochastic gradient descent: Machine learning for parametric PDEs and financial derivative pricing (Q6178392) (← links)
• Pricing options on flow forwards by neural networks in a Hilbert space (Q6181517) (← links)
• Nonparametric inference of stochastic differential equations based on the relative entropy rate (Q6182259) (← links)
• A priori error estimate of deep mixed residual method for elliptic PDEs (Q6182315) (← links)
• Deep Ritz method for elliptical multiple eigenvalue problems (Q6182319) (← links)
• Feature engineering with regularity structures (Q6184277) (← links)
• Learning High-Dimensional McKean–Vlasov Forward-Backward Stochastic Differential Equations with General Distribution Dependence (Q6184510) (← links)
• A deep-genetic algorithm (deep-GA) approach for high-dimensional nonlinear parabolic partial differential equations (Q6184720) (← links)
• A nonlinear-manifold reduced-order model and operator learning for partial differential equations with sharp solution gradients (Q6185246) (← links)
• Multi-output physics-informed neural network for one- and two-dimensional nonlinear time distributed-order models (Q6186170) (← links)
• Fast and scalable computation of shape-morphing nonlinear solutions with application to evolutional neural networks (Q6187618) (← links)
• The use of physics-informed neural network approach to image restoration via nonlinear PDE tools (Q6189287) (← links)
• Less Emphasis on Hard Regions: Curriculum Learning of PINNs for Singularly Perturbed Convection-Diffusion-Reaction Problems (Q6192635) (← links)
• Sequential propagation of chaos for mean-field BSDE systems (Q6194041) (← links)
• Spectral operator learning for parametric PDEs without data reliance (Q6194143) (← links)
• The random feature method for solving interface problems (Q6194187) (← links)
• AONN: An Adjoint-Oriented Neural Network Method for All-At-Once Solutions of Parametric Optimal Control Problems (Q6194971) (← links)
• Neural Control of Parametric Solutions for High-Dimensional Evolution PDEs (Q6194975) (← links)
• Reinforcement learning with dynamic convex risk measures (Q6196296) (← links)
• A deep branching solver for fully nonlinear partial differential equations (Q6196609) (← links)
• The mathematics of artificial intelligence (Q6200206) (← links)
• Solving inverse problems with deep learning (Q6200208) (← links)
• Deep learning algorithms for solving high-dimensional nonlinear backward stochastic differential equations (Q6201366) (← links)
• Deep learning approximations for non-local nonlinear PDEs with Neumann boundary conditions (Q6204733) (← links)
• Simultaneous approximation of a smooth function and its derivatives by deep neural networks with piecewise-polynomial activations (Q6402542) (← links)
• Basis operator network: a neural network-based model for learning nonlinear operators via neural basis (Q6488825) (← links)
• Iterative schemes for probabilistic domain decomposition (Q6491443) (← links)
• CCGnet: a deep learning approach to predict Nash equilibrium of chance-constrained games (Q6492614) (← links)
• Deep signature algorithm for multidimensional path-dependent options (Q6496949) (← links)
• Efficient and stable SAV-based methods for gradient flows arising from deep learning (Q6497260) (← links)