Variable linear transformation improved physics-informed neural networks to solve thin-layer flow problems
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Publication:6119307
DOI10.1016/j.jcp.2024.112761OpenAlexW4390674397MaRDI QIDQ6119307
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Publication date: 29 February 2024
Published in: Journal of Computational Physics (Search for Journal in Brave)
Full work available at URL: https://doi.org/10.1016/j.jcp.2024.112761
boundary layerwake flowmixing layerjet flowphysics-informed neural networks (PINNs)differential equations (DEs)
Artificial intelligence (68Txx) Numerical methods for partial differential equations, initial value and time-dependent initial-boundary value problems (65Mxx) Numerical analysis (65-XX)
Cites Work
- Machine learning in cardiovascular flows modeling: predicting arterial blood pressure from non-invasive 4D flow MRI data using physics-informed neural networks
- PPINN: parareal physics-informed neural network for time-dependent PDEs
- A physics-informed deep learning framework for inversion and surrogate modeling in solid mechanics
- PhyCRNet: physics-informed convolutional-recurrent network for solving spatiotemporal PDEs
- Theory-guided physics-informed neural networks for boundary layer problems with singular perturbation
- NSFnets (Navier-Stokes flow nets): physics-informed neural networks for the incompressible Navier-Stokes equations
- PhyGeoNet: physics-informed geometry-adaptive convolutional neural networks for solving parameterized steady-state PDEs on irregular domain
- Scientific machine learning through physics-informed neural networks: where we are and what's next
- Conservative physics-informed neural networks on discrete domains for conservation laws: applications to forward and inverse problems
- Adaptive activation functions accelerate convergence in deep and physics-informed neural networks
- Physics-informed multi-LSTM networks for metamodeling of nonlinear structures
- Physics-informed neural networks: a deep learning framework for solving forward and inverse problems involving nonlinear partial differential equations
- Boundary-Layer Theory
- Understanding and Mitigating Gradient Flow Pathologies in Physics-Informed Neural Networks
- DeepXDE: A Deep Learning Library for Solving Differential Equations
- Extended Physics-Informed Neural Networks (XPINNs): A Generalized Space-Time Domain Decomposition Based Deep Learning Framework for Nonlinear Partial Differential Equations
- Physics-Informed Neural Networks with Hard Constraints for Inverse Design
- Physics-Informed Generative Adversarial Networks for Stochastic Differential Equations
- THE VELOCITY DISTRIBUTION IN THE LAMINAR BOUNDARY LAYER BETWEEN PARALLEL STREAMS
