Pages that link to "Item:Q4967451"

The following pages link to Solving high-dimensional partial differential equations using deep learning (Q4967451):

Displaying 50 items.

• Linearized Learning with Multiscale Deep Neural Networks for Stationary Navier-Stokes Equations with Oscillatory Solutions (Q6069455) (← links)
• A Neural Network Approach to High-Dimensional Optimal Switching Problems with Jumps in Energy Markets (Q6070671) (← links)
• Pricing Bermudan Options Using Regression Trees/Random Forests (Q6070674) (← links)
• Deep learning methods for partial differential equations and related parameter identification problems (Q6070739) (← links)
• Numerical solution of the modified and non-Newtonian Burgers equations by stochastic coded trees (Q6072375) (← links)
• Optimal polynomial feedback laws for finite horizon control problems (Q6072899) (← links)
• Some elliptic second order problems and neural network solutions: existence and error estimates (Q6073185) (← links)
• A priori generalization error analysis of two-layer neural networks for solving high dimensional Schrödinger eigenvalue problems (Q6076649) (← links)
• A physics-constrained deep residual network for solving the sine-Gordon equation (Q6076683) (← links)
• Deep neural network solution for finite state mean field game with error estimation (Q6078100) (← links)
• Neural networks for first order HJB equations and application to front propagation with obstacle terms (Q6087416) (← links)
• Adaptive deep density approximation for fractional Fokker-Planck equations (Q6087826) (← links)
• The Random Feature Method for Time-Dependent Problems (Q6090342) (← links)
• A deep learning method for solving third-order nonlinear evolution equations (Q6094544) (← links)
• Dosnet as a non-black-box PDE solver: when deep learning meets operator splitting (Q6095076) (← links)
• On the use of neural networks for full waveform inversion (Q6096500) (← links)
• Physics-informed deep learning for simultaneous surrogate modeling and PDE-constrained optimization of an airfoil geometry (Q6097587) (← links)
• Spiking recurrent neural networks for neuromorphic computing in nonlinear structural mechanics (Q6097653) (← links)
• Deep learning for thermal plasma simulation: solving 1-D arc model as an example (Q6097959) (← links)
• XVA in a multi-currency setting with stochastic foreign exchange rates (Q6102925) (← links)
• Data-driven vortex solitons and parameter discovery of 2D generalized nonlinear Schrödinger equations with a \(\mathcal{PT}\)-symmetric optical lattice (Q6103701) (← links)
• The robust physics-informed neural networks for a typical fourth-order phase field model (Q6103706) (← links)
• Predicting rare events using neural networks and short-trajectory data (Q6104999) (← links)
• Multigrid-Augmented Deep Learning Preconditioners for the Helmholtz Equation (Q6108146) (← links)
• Learning Optimal Multigrid Smoothers via Neural Networks (Q6108149) (← links)
• Friedrichs Learning: Weak Solutions of Partial Differential Equations via Deep Learning (Q6108164) (← links)
• Machine Learning Moment Closure Models for the Radiative Transfer Equation II: Enforcing Global Hyperbolicity in Gradient-Based Closures (Q6109133) (← links)
• A Hybrid Method for Three-Dimensional Semi-Linear Elliptic Equations (Q6110111) (← links)
• The Kolmogorov infinite dimensional equation in a Hilbert space via deep learning methods (Q6112485) (← links)
• A deep learning approach to the probabilistic numerical solution of path-dependent partial differential equations (Q6114174) (← links)
• Neural Galerkin schemes with active learning for high-dimensional evolution equations (Q6117685) (← links)
• A mathematical perspective of machine learning (Q6118171) (← links)
• An extreme learning machine-based method for computational PDEs in higher dimensions (Q6120177) (← links)
• Limitations of neural network training due to numerical instability of backpropagation (Q6122651) (← links)
• Physics-based self-learning spiking neural network enhanced time-integration scheme for computing viscoplastic structural finite element response (Q6125507) (← links)
• Forward to the special topic on ``Solving differential equations with deep learning'' (Q6130975) (← links)
• Number of solitons emerged in the initial profile of shallow water using convolutional neural networks (Q6130982) (← links)
• Random vibration of hysteretic systems under Poisson white noise excitations (Q6132251) (← links)
• Variational inference in neural functional prior using normalizing flows: application to differential equation and operator learning problems (Q6132292) (← links)
• Linear Convergence of a Policy Gradient Method for Some Finite Horizon Continuous Time Control Problems (Q6140987) (← links)
• DPK: Deep Neural Network Approximation of the First Piola-Kirchhoff Stress (Q6141664) (← links)
• A Novel Deep Neural Network Algorithm for the Helmholtz Scattering Problem In the Unbounded Domain (Q6143278) (← links)
• A Variational Neural Network Approach for Glacier Modelling with Nonlinear Rheology (Q6143617) (← links)
• Deep neural networks learning forward and inverse problems of two-dimensional nonlinear wave equations with rational solitons (Q6143642) (← links)
• Convergence of the Backward Deep BSDE Method with Applications to Optimal Stopping Problems (Q6143826) (← links)
• \(r\)-adaptive deep learning method for solving partial differential equations (Q6144172) (← links)
• Variable separated physics-informed neural networks based on adaptive weighted loss functions for blood flow model (Q6144182) (← links)
• Error analysis of deep Ritz methods for elliptic equations (Q6145797) (← links)
• Biomimetic IGA neuron growth modeling with neurite morphometric features and CNN-based prediction (Q6146998) (← links)
• Label-free learning of elliptic partial differential equation solvers with generalizability across boundary value problems (Q6146999) (← links)