Pages that link to "Item:Q2002333"
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The following pages link to DGM: a deep learning algorithm for solving partial differential equations (Q2002333):
Displaying 50 items.
- An unsupervised deep learning approach to solving partial integro-differential equations (Q5092661) (← links)
- Deep neural network approximation for high-dimensional elliptic PDEs with boundary conditions (Q5093100) (← links)
- Unbiased Deep Solvers for Linear Parametric PDEs (Q5093244) (← links)
- (Q5093541) (← links)
- Deep Neural Networks and PIDE Discretizations (Q5100094) (← 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)
- 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)
- Overcoming the curse of dimensionality in the numerical approximation of semilinear parabolic partial differential equations (Q5161194) (← 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)
- Solving inverse problems using data-driven models (Q5230520) (← links)
- Deep learning for limit order books (Q5234311) (← links)
- Deep optimal stopping (Q5381128) (← links)
- Strong Solutions for PDE-Based Tomography by Unsupervised Learning (Q5860278) (← links)
- A Local Deep Learning Method for Solving High Order Partial Differential Equations (Q5864768) (← links)
- Deep neural networks can stably solve high-dimensional, noisy, non-linear inverse problems (Q5873926) (← links)
- Stationary Density Estimation of Itô Diffusions Using Deep Learning (Q5886225) (← links)
- Efficient Time-Stepping for Numerical Integration Using Reinforcement Learning (Q6039265) (← links)
- A deep learning method for solving high-order nonlinear soliton equations (Q6039957) (← links)
- A physics-informed neural network technique based on a modified loss function for computational 2D and 3D solid mechanics (Q6044222) (← links)
- Overcoming the timescale barrier in molecular dynamics: Transfer operators, variational principles and machine learning (Q6047503) (← links)
- Data-driven hedging of stock index options via deep learning (Q6047693) (← links)
- Solving second-order nonlinear evolution partial differential equations using deep learning (Q6048188) (← links)
- Learning-based local weighted least squares for algebraic multigrid method (Q6048416) (← links)
- Multifidelity deep operator networks for data-driven and physics-informed problems (Q6048427) (← links)
- JAX-DIPS: neural bootstrapping of finite discretization methods and application to elliptic problems with discontinuities (Q6048459) (← links)
- Numerical Solution of the Incompressible Navier-Stokes Equation by a Deep Branching Algorithm (Q6049610) (← links)
- Neural Networks with Local Converging Inputs (NNLCI) for Solving Conservation Laws, Part I: 1D Problems (Q6049611) (← links)
- Deep learning-accelerated computational framework based on physics informed neural network for the solution of linear elasticity (Q6053463) (← links)
- Solving multiscale elliptic problems by sparse radial basis function neural networks (Q6054222) (← links)
