Pages that link to "Item:Q5014839"
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The following pages link to Solving parametric PDE problems with artificial neural networks (Q5014839):
Displaying 50 items.
- Automated design parameter selection for neural networks solving coupled partial differential equations with discontinuities (Q388540) (← links)
- Traveling wave solutions of partial differential equations via neural networks (Q1983171) (← links)
- Networks for nonlinear diffusion problems in imaging (Q1988365) (← links)
- A novel sequential method to train physics informed neural networks for Allen Cahn and Cahn Hilliard equations (Q2072734) (← links)
- Monte Carlo fPINNs: deep learning method for forward and inverse problems involving high dimensional fractional partial differential equations (Q2083146) (← links)
- Learning phase field mean curvature flows with neural networks (Q2083658) (← links)
- Efficient coupled deep neural networks for the time-dependent coupled Stokes-Darcy problems (Q2096255) (← links)
- CAS4DL: Christoffel adaptive sampling for function approximation via deep learning (Q2098302) (← links)
- Committor functions via tensor networks (Q2099715) (← links)
- Solving parametric partial differential equations with deep rectified quadratic unit neural networks (Q2103467) (← links)
- B-DeepONet: an enhanced Bayesian deeponet for solving noisy parametric PDEs using accelerated replica exchange SGLD (Q2106911) (← links)
- DNN expression rate analysis of high-dimensional PDEs: application to option pricing (Q2117328) (← links)
- A theoretical analysis of deep neural networks and parametric PDEs (Q2117329) (← links)
- A fast and accurate physics-informed neural network reduced order model with shallow masked autoencoder (Q2134764) (← links)
- Normalizing field flows: solving forward and inverse stochastic differential equations using physics-informed flow models (Q2138012) (← links)
- On quadrature rules for solving partial differential equations using neural networks (Q2138756) (← links)
- Computing the invariant distribution of randomly perturbed dynamical systems using deep learning (Q2149015) (← links)
- Deep neural network approximations for solutions of PDEs based on Monte Carlo algorithms (Q2152480) (← links)
- Learning deep implicit Fourier neural operators (IFNOs) with applications to heterogeneous material modeling (Q2160481) (← links)
- Deep global model reduction learning in porous media flow simulation (Q2187913) (← links)
- Solving many-electron Schrödinger equation using deep neural networks (Q2222630) (← links)
- Artificial neural network approximations of Cauchy inverse problem for linear PDEs (Q2247118) (← links)
- General solutions for nonlinear differential equations: a rule-based self-learning approach using deep reinforcement learning (Q2281483) (← links)
- A multiscale neural network based on hierarchical nested bases (Q2319969) (← links)
- Machine learning approximation algorithms for high-dimensional fully nonlinear partial differential equations and second-order backward stochastic differential equations (Q2327815) (← links)
- A finite element based deep learning solver for parametric PDEs (Q2670366) (← links)
- DeepParticle: learning invariant measure by a deep neural network minimizing Wasserstein distance on data generated from an interacting particle method (Q2672762) (← links)
- A survey of unsupervised learning methods for high-dimensional uncertainty quantification in black-box-type problems (Q2672767) (← links)
- Nonlocal kernel network (NKN): a stable and resolution-independent deep neural network (Q2675608) (← links)
- ADLGM: an efficient adaptive sampling deep learning Galerkin method (Q2683243) (← links)
- opPINN: physics-informed neural network with operator learning to approximate solutions to the Fokker-Planck-Landau equation (Q2689626) (← links)
- Greedy training algorithms for neural networks and applications to PDEs (Q2699382) (← links)
- Efficient Construction of Tensor Ring Representations from Sampling (Q5006479) (← links)
- Connections between deep learning and partial differential equations (Q5014844) (← links)
- Deep Adaptive Basis Galerkin Method for High-Dimensional Evolution Equations With Oscillatory Solutions (Q5038412) (← links)
- Neural Parametric Fokker--Planck Equation (Q5087103) (← links)
- Physics-Informed Generative Adversarial Networks for Stochastic Differential Equations (Q5214836) (← links)
- A Multiscale Neural Network Based on Hierarchical Matrices (Q5222107) (← links)
- Solving inverse problems using data-driven models (Q5230520) (← links)
- Multilevel Fine-Tuning: Closing Generalization Gaps in Approximation of Solution Maps under a Limited Budget for Training Data (Q5857926) (← links)
- Stationary Density Estimation of Itô Diffusions Using Deep Learning (Q5886225) (← links)
- A Proof that Artificial Neural Networks Overcome the Curse of Dimensionality in the Numerical Approximation of Black–Scholes Partial Differential Equations (Q5889064) (← links)
- Simultaneous neural network approximation for smooth functions (Q6052416) (← links)
- Probabilistic partition of unity networks for high‐dimensional regression problems (Q6062830) (← links)
- Deep capsule encoder–decoder network for surrogate modeling and uncertainty quantification (Q6082494) (← links)
- Solving nonconvex energy minimization problems in martensitic phase transitions with a mesh-free deep learning approach (Q6084532) (← links)
- Data-driven vortex solitons and parameter discovery of 2D generalized nonlinear Schrödinger equations with a \(\mathcal{PT}\)-symmetric optical lattice (Q6103701) (← links)
- A method for computing inverse parametric PDE problems with random-weight neural networks (Q6107102) (← links)
- Multigrid-Augmented Deep Learning Preconditioners for the Helmholtz Equation (Q6108146) (← links)
- Friedrichs Learning: Weak Solutions of Partial Differential Equations via Deep Learning (Q6108164) (← links)
