Simple computational strategies for more effective physics-informed neural networks modeling of turbulent natural convection
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Publication:2133780
DOI10.1016/j.jcp.2022.111022OpenAlexW4210771111MaRDI QIDQ2133780
Anne Sergent, Atul Agrawal, Didier Lucor
Publication date: 5 May 2022
Published in: Journal of Computational Physics (Search for Journal in Brave)
Full work available at URL: https://doi.org/10.1016/j.jcp.2022.111022
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- Uncertainty quantification in computational fluid dynamics
- Machine learning strategies for systems with invariance properties
- Numerical simulation of fluid-structure interaction with the volume penalization method
- The numerical solution of linear ordinary differential equations by feedforward neural networks
- SciANN: a Keras/Tensorflow wrapper for scientific computations and physics-informed deep learning using artificial neural networks
- B-PINNs: Bayesian physics-informed neural networks for forward and inverse PDE problems with noisy data
- Physics-informed neural networks for high-speed flows
- Surrogate modeling for fluid flows based on physics-constrained deep learning without simulation data
- Physics-informed neural networks: a deep learning framework for solving forward and inverse problems involving nonlinear partial differential equations
- An overview of projection methods for incompressible flows
- Multilayer perceptrons and radial basis function neural network methods for the solution of differential equations: a survey
- Machine Learning for Fluid Mechanics
- On the role of roughness valleys in turbulent Rayleigh–Bénard convection
- Understanding and Mitigating Gradient Flow Pathologies in Physics-Informed Neural Networks
- Prediction of turbulent heat transfer using convolutional neural networks
- Hidden fluid mechanics: Learning velocity and pressure fields from flow visualizations
- Deep learning in turbulent convection networks
- Turbulent temperature fluctuations in a closed Rayleigh–Bénard convection cell
- Cessation and reversals of large-scale structures in square Rayleigh–Bénard cells
- Learning representations by back-propagating errors
- Deep learning in fluid dynamics
- Turbulence Modeling in the Age of Data
