The following pages link to DGM (Q54982):
Displaying 50 items.
- A finite element based deep learning solver for parametric PDEs (Q2670366) (← links)
- Adaptive deep neural networks methods for high-dimensional partial differential equations (Q2671349) (← links)
- Towards fast weak adversarial training to solve high dimensional parabolic partial differential equations using XNODE-WAN (Q2671351) (← links)
- Wasserstein generative adversarial uncertainty quantification in physics-informed neural networks (Q2671386) (← links)
- Physics and equality constrained artificial neural networks: application to forward and inverse problems with multi-fidelity data fusion (Q2671417) (← links)
- DeepParticle: learning invariant measure by a deep neural network minimizing Wasserstein distance on data generated from an interacting particle method (Q2672762) (← links)
- Numerical solution of the Fokker-Planck equation using physics-based mixture models (Q2674128) (← links)
- The deep learning Galerkin method for the general Stokes equations (Q2674271) (← links)
- A shallow Ritz method for elliptic problems with singular sources (Q2675616) (← links)
- A discontinuity capturing shallow neural network for elliptic interface problems (Q2675625) (← links)
- Galerkin neural network approximation of singularly-perturbed elliptic systems (Q2679286) (← links)
- Neural control of discrete weak formulations: Galerkin, least squares \& minimal-residual methods with quasi-optimal weights (Q2679332) (← links)
- A deep first-order system least squares method for solving elliptic PDEs (Q2679352) (← links)
- SVD perspectives for augmenting DeepONet flexibility and interpretability (Q2679470) (← links)
- Modeling systems with machine learning based differential equations (Q2680007) (← links)
- Solving non-linear Kolmogorov equations in large dimensions by using deep learning: a numerical comparison of discretization schemes (Q2680327) (← links)
- DAS-PINNs: a deep adaptive sampling method for solving high-dimensional partial differential equations (Q2681099) (← links)
- Uncertainty quantification in scientific machine learning: methods, metrics, and comparisons (Q2681129) (← links)
- Inverse stochastic optimal controls (Q2681368) (← links)
- CPINNs: a coupled physics-informed neural networks for the closed-loop geothermal system (Q2682678) (← links)
- Active learning based sampling for high-dimensional nonlinear partial differential equations (Q2683063) (← links)
- Space-time error estimates for deep neural network approximations for differential equations (Q2683168) (← links)
- ADLGM: an efficient adaptive sampling deep learning Galerkin method (Q2683243) (← links)
- Isogeometric neural networks: a new deep learning approach for solving parameterized partial differential equations (Q2683423) (← links)
- A deep Fourier residual method for solving PDEs using neural networks (Q2683430) (← links)
- A deep double Ritz method (\(\mathrm{D^2RM}\)) for solving partial differential equations using neural networks (Q2683471) (← links)
- Neural network architectures using min-plus algebra for solving certain high-dimensional optimal control problems and Hamilton-Jacobi PDEs (Q2683498) (← links)
- Stochastic projection based approach for gradient free physics informed learning (Q2686876) (← links)
- QBoost for regression problems: solving partial differential equations (Q2687371) (← links)
- 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)
- MFO-RIMS tandem workshop: Nonlocality in analysis, probability and statistics. Abstracts from the MFO-RIMS tandem workshop held March 20--26, 2022 (Q2693042) (← 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)
- 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)
