Pages that link to "Item:Q4967451"
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The following pages link to Solving high-dimensional partial differential equations using deep learning (Q4967451):
Displaying 50 items.
- Numerical computation of probabilities for nonlinear SDEs in high dimension using Kolmogorov equation (Q2673974) (← links)
- Scalable uncertainty quantification for deep operator networks using randomized priors (Q2674111) (← links)
- Numerical solution of the Fokker-Planck equation using physics-based mixture models (Q2674128) (← 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)
- Solving stochastic optimal control problem via stochastic maximum principle with deep learning method (Q2676795) (← links)
- Kolmogorov n-width and Lagrangian physics-informed neural networks: a causality-conforming manifold for convection-dominated PDEs (Q2678525) (← links)
- Multi-fidelity surrogate modeling using long short-term memory networks (Q2678526) (← links)
- Neural networks in Fréchet spaces (Q2679424) (← links)
- Machine learning moment closure models for the radiative transfer equation. III: enforcing hyperbolicity and physical characteristic speeds (Q2680325) (← links)
- Solving non-linear Kolmogorov equations in large dimensions by using deep learning: a numerical comparison of discretization schemes (Q2680327) (← links)
- New regularity estimates for fully nonlinear elliptic equations (Q2681050) (← links)
- DAS-PINNs: a deep adaptive sampling method for solving high-dimensional partial differential equations (Q2681099) (← links)
- A physics-informed convolutional neural network for the simulation and prediction of two-phase Darcy flows in heterogeneous porous media (Q2681146) (← links)
- Surrogate modeling for Bayesian inverse problems based on physics-informed neural networks (Q2683056) (← links)
- Active learning based sampling for high-dimensional nonlinear partial differential equations (Q2683063) (← links)
- Long-time integration of parametric evolution equations with physics-informed DeepONets (Q2683074) (← 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)
- Transfer learning based physics-informed neural networks for solving inverse problems in engineering structures under different loading scenarios (Q2683433) (← links)
- Neural network architectures using min-plus algebra for solving certain high-dimensional optimal control problems and Hamilton-Jacobi PDEs (Q2683498) (← links)
- Optimal control by deep learning techniques and its applications on epidemic models (Q2684035) (← links)
- Numerical resolution of McKean-Vlasov FBSDEs using neural networks (Q2684929) (← links)
- Data-driven control of agent-based models: an equation/variable-free machine learning approach (Q2687520) (← 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)
- State-dependent Riccati equation feedback stabilization for nonlinear PDEs (Q2692793) (← links)
- Mini-workshop: Analysis of data-driven optimal control. Abstracts from the mini-workshop held May 9--15, 2021 (hybrid meeting) (Q2693004) (← links)
- An unsupervised latent/output physics-informed convolutional-LSTM network for solving partial differential equations using peridynamic differential operator (Q2693426) (← links)
- Control of partial differential equations via physics-informed neural networks (Q2696946) (← links)
- Approximation properties of residual neural networks for Kolmogorov PDEs (Q2697245) (← 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)
- Solving traveltime tomography with deep learning (Q2699488) (← links)
- Neural network-based variational methods for solving quadratic porous medium equations in high dimensions (Q2699489) (← 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)
- Path-Dependent Deep Galerkin Method: A Neural Network Approach to Solve Path-Dependent Partial Differential Equations (Q4958400) (← links)
- Deep Splitting Method for Parabolic PDEs (Q4958922) (← links)
- Deep backward schemes for high-dimensional nonlinear PDEs (Q4960067) (← links)
- Low-Dimensional Approximations of High-Dimensional Asset Price Models (Q4990516) (← links)
- Machine learning for pricing American options in high-dimensional Markovian and non-Markovian models (Q4991044) (← links)
- Data informed solution estimation for forward-backward stochastic differential equations (Q4995043) (← 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)
- Partial differential equation regularization for supervised machine learning (Q4998637) (← links)
- (Q4998939) (← links)
- Enhancing Accuracy of Deep Learning Algorithms by Training with Low-Discrepancy Sequences (Q5001377) (← links)
- Solving Inverse Stochastic Problems from Discrete Particle Observations Using the Fokker--Planck Equation and Physics-Informed Neural Networks (Q5004999) (← links)
- Galerkin Neural Networks: A Framework for Approximating Variational Equations with Error Control (Q5005011) (← links)
