The following pages link to DGM (Q54982):
Displaying 50 items.
- Learning and meta-learning of stochastic advection–diffusion–reaction systems from sparse measurements (Q5014838) (← links)
- Solving high-dimensional optimal stopping problems using deep learning (Q5014845) (← links)
- (Q5019878) (← links)
- Algorithms for solving high dimensional PDEs: from nonlinear Monte Carlo to machine learning (Q5019943) (← links)
- Augmenting physical models with deep networks for complex dynamics forecasting* (Q5020055) (← links)
- Approximation Error Analysis of Some Deep Backward Schemes for Nonlinear PDEs (Q5021399) (← links)
- Optimization with learning-informed differential equation constraints and its applications (Q5024338) (← links)
- Numerical valuation of Bermudan basket options via partial differential equations (Q5031294) (← 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)
- Deep Adaptive Basis Galerkin Method for High-Dimensional Evolution Equations With Oscillatory Solutions (Q5038412) (← 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)
- Deep Domain Decomposition Methods: Helmholtz Equation (Q5045688) (← links)
- MIONet: Learning Multiple-Input Operators via Tensor Product (Q5048574) (← links)
- Learning a functional control for high-frequency finance (Q5051970) (← links)
- A deep neural network-based numerical method for solving contact problems (Q5052594) (← links)
- SympOCnet: Solving Optimal Control Problems with Applications to High-Dimensional Multiagent Path Planning Problems (Q5058288) (← links)
- VPVnet: A Velocity-Pressure-Vorticity Neural Network Method for the Stokes’ Equations under Reduced Regularity (Q5065192) (← links)
- An Augmented Lagrangian Deep Learning Method for Variational Problems with Essential Boundary Conditions (Q5065200) (← links)
- (Q5066183) (← links)
- A Deep Learning Method for Elliptic Hemivariational Inequalities (Q5074898) (← links)
- Approximations with deep neural networks in Sobolev time-space (Q5075578) (← links)
- Mean Field Analysis of Deep Neural Networks (Q5076694) (← links)
- Convergence Rate Analysis for Deep Ritz Method (Q5077692) (← 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)
- Deep Neural Networks and PIDE Discretizations (Q5100094) (← 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)
- Optimizing a portfolio of mean-reverting assets with transaction costs via a feedforward neural network (Q5139230) (← links)
- Neural network representation of the probability density function of diffusion processes (Q5139752) (← links)
- DeepXDE: A Deep Learning Library for Solving Differential Equations (Q5150214) (← links)
- Finite Neuron Method and Convergence Analysis (Q5162357) (← links)
- A Multi-Scale DNN Algorithm for Nonlinear Elliptic Equations with Multiple Scales (Q5162363) (← links)
- Extended Physics-Informed Neural Networks (XPINNs): A Generalized Space-Time Domain Decomposition Based Deep Learning Framework for Nonlinear Partial Differential Equations (Q5162369) (← links)
- On the Convergence of Physics Informed Neural Networks for Linear Second-Order Elliptic and Parabolic Type PDEs (Q5162370) (← links)
- Numerical Simulations for Full History Recursive Multilevel Picard Approximations for Systems of High-Dimensional Partial Differential Equations (Q5162373) (← links)
- Learning to Discretize: Solving 1D Scalar Conservation Laws via Deep Reinforcement Learning (Q5162375) (← links)
- Train Like a (Var)Pro: Efficient Training of Neural Networks with Variable Projection (Q5162626) (← links)
- Solving Allen-Cahn and Cahn-Hilliard Equations using the Adaptive Physics Informed Neural Networks (Q5163210) (← links)
- Deep Nitsche Method: Deep Ritz Method with Essential Boundary Conditions (Q5163229) (← links)
- The model reduction of the Vlasov–Poisson–Fokker–Planck system to the Poisson–Nernst–Planck system <i>via</i> the Deep Neural Network Approach (Q5163496) (← links)
- Enforcing Imprecise Constraints on Generative Adversarial Networks for Emulating Physical Systems (Q5163887) (← links)
- Learning the tangent space of dynamical instabilities from data (Q5205672) (← links)
- Physics-Informed Generative Adversarial Networks for Stochastic Differential Equations (Q5214836) (← links)
- Solving Fokker-Planck equation using deep learning (Q5218164) (← links)
- Mean Field Analysis of Neural Networks: A Law of Large Numbers (Q5219306) (← links)
- A Multiscale Neural Network Based on Hierarchical Matrices (Q5222107) (← links)
