FDM-PINN: Physics-informed neural network based on fictitious domain method
From MaRDI portal
Publication:6103283
DOI10.1080/00207160.2022.2128674zbMath1524.76008MaRDI QIDQ6103283
No author found.
Publication date: 26 June 2023
Published in: International Journal of Computer Mathematics (Search for Journal in Brave)
neural networkautomatic differentiationfictitious domain methodRobin boundary conditionelliptic problem
Cites Work
- Unnamed Item
- When are quasi-Monte Carlo algorithms efficient for high dimensional integrals?
- On Latin hypercube sampling
- A least-squares/fictitious domain method for incompressible viscous flow around obstacles with Navier slip boundary condition
- A fictitious domain method for Dirichlet problem and applications
- DGM: a deep learning algorithm for solving partial differential equations
- B-PINNs: Bayesian physics-informed neural networks for forward and inverse PDE problems with noisy data
- When and why PINNs fail to train: a neural tangent kernel perspective
- Gradient-enhanced physics-informed neural networks for forward and inverse PDE problems
- Adversarial uncertainty quantification in physics-informed neural networks
- Adaptive activation functions accelerate convergence in deep and physics-informed neural networks
- The least-square/fictitious domain method based on Navier slip boundary condition for simulation of flow-particle interaction
- Physics-informed neural networks: a deep learning framework for solving forward and inverse problems involving nonlinear partial differential equations
- Neural algorithm for solving differential equations
- Wasserstein generative adversarial uncertainty quantification in physics-informed neural networks
- DeepXDE: A Deep Learning Library for Solving Differential Equations
- Locally adaptive activation functions with slope recovery for deep and physics-informed neural networks
- Physics-Informed Neural Networks with Hard Constraints for Inverse Design
- Learning in Modal Space: Solving Time-Dependent Stochastic PDEs Using Physics-Informed Neural Networks
- fPINNs: Fractional Physics-Informed Neural Networks
- A Least-Squares/Fictitious Domain Method for Linear Elliptic Problems with Robin Boundary Conditions
- A distributed Lagrange multiplier/fictitious domain method for particulate flows
- Non-iterative numerical solution of boundary-value problems
This page was built for publication: FDM-PINN: Physics-informed neural network based on fictitious domain method
