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.
- opPINN: physics-informed neural network with operator learning to approximate solutions to the Fokker-Planck-Landau equation (Q2689626) (← links)
- A fully nonlinear Feynman-Kac formula with derivatives of arbitrary orders (Q2690084) (← links)
- Time difference physics-informed neural network for fractional water wave models (Q2690093) (← links)
- Multi-scale fusion network: a new deep learning structure for elliptic interface problems (Q2691986) (← links)
- Mini-workshop: Analysis of data-driven optimal control. Abstracts from the mini-workshop held May 9--15, 2021 (hybrid meeting) (Q2693004) (← links)
- Physics-informed neural networks based on adaptive weighted loss functions for Hamilton-Jacobi equations (Q2694112) (← links)
- An overview on deep learning-based approximation methods for partial differential equations (Q2697278) (← links)
- Greedy training algorithms for neural networks and applications to PDEs (Q2699382) (← links)
- BI-GreenNet: learning Green's functions by boundary integral network (Q2699491) (← links)
- Robust Feedback Control of Nonlinear PDEs by Numerical Approximation of High-Dimensional Hamilton--Jacobi--Isaacs Equations (Q3298341) (← links)
- The Random Feature Model for Input-Output Maps between Banach Spaces (Q3382802) (← links)
- Flow over an espresso cup: inferring 3-D velocity and pressure fields from tomographic background oriented Schlieren via physics-informed neural networks (Q3389009) (← links)
- Stochastic Gradient Descent in Continuous Time (Q4607057) (← links)
- Path-Dependent Deep Galerkin Method: A Neural Network Approach to Solve Path-Dependent Partial Differential Equations (Q4958400) (← links)
- Understanding and Mitigating Gradient Flow Pathologies in Physics-Informed Neural Networks (Q4958918) (← links)
- Deep Splitting Method for Parabolic PDEs (Q4958922) (← links)
- Deep backward schemes for high-dimensional nonlinear PDEs (Q4960067) (← links)
- Convergence Analysis of Machine Learning Algorithms for the Numerical Solution of Mean Field Control and Games I: The Ergodic Case (Q4994415) (← links)
- Adaptive Deep Learning for High-Dimensional Hamilton--Jacobi--Bellman Equations (Q4997364) (← links)
- Tensor Decomposition Methods for High-dimensional Hamilton--Jacobi--Bellman Equations (Q4997370) (← links)
- Galerkin Neural Networks: A Framework for Approximating Variational Equations with Error Control (Q5005011) (← links)
- Deep neural network framework based on backward stochastic differential equations for pricing and hedging American options in high dimensions (Q5014169) (← links)
- 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)
- Generalized Cell Mapping Method with Deep Learning for Global Analysis and Response Prediction of Dynamical Systems (Q5016866) (← 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)
- Imaging conductivity from current density magnitude using neural networks* (Q5081798) (← links)
