Multi-Fidelity Machine Learning Applied to Steady Fluid Flows
DOI10.1080/10618562.2022.2154758OpenAlexW4320082324MaRDI QIDQ5880416
Kazuko Fuchi, Christopher R. Schrock, David Makhija, Philip S. Beran, Eric M. Wolf
Publication date: 2 March 2023
Published in: International Journal of Computational Fluid Dynamics (Search for Journal in Brave)
Full work available at URL: https://doi.org/10.1080/10618562.2022.2154758
small datamachine learningcylinder flowJoukowski airfoilboundary geometry changeCFD initialisationelliptic input featurepartition-of-unity extension
Learning and adaptive systems in artificial intelligence (68T05) Wakes and jets (76D25) Basic methods in fluid mechanics (76M99)
Related Items (1)
Uses Software
Cites Work
- Unnamed Item
- A review of variational multiscale methods for the simulation of turbulent incompressible flows
- Mosaic flows: a transferable deep learning framework for solving PDEs on unseen domains
- 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
- Gradient-enhanced physics-informed neural networks for forward and inverse PDE problems
- CAN-PINN: a fast physics-informed neural network based on coupled-automatic-numerical differentiation method
- Physics-informed neural networks for inverse problems in supersonic flows
- Scientific machine learning through physics-informed neural networks: where we are and what's next
- Physics-informed neural networks for high-speed flows
- Surrogate modeling for fluid flows based on physics-constrained deep learning without simulation data
- Conservative physics-informed neural networks on discrete domains for conservation laws: applications to forward and inverse problems
- A turbulent eddy-viscosity surrogate modeling framework for Reynolds-averaged Navier-Stokes simulations
- Physics-informed neural networks: a deep learning framework for solving forward and inverse problems involving nonlinear partial differential equations
- Physics and equality constrained artificial neural networks: application to forward and inverse problems with multi-fidelity data fusion
- A comprehensive study of non-adaptive and residual-based adaptive sampling for physics-informed neural networks
- A metalearning approach for physics-informed neural networks (PINNs): application to parameterized PDEs
- Machine Learning for Fluid Mechanics
- Gmsh: A 3-D finite element mesh generator with built-in pre- and post-processing facilities
- Hidden fluid mechanics: Learning velocity and pressure fields from flow visualizations
- Multi-Fidelity Machine Learning Applied to Steady Fluid Flows
This page was built for publication: Multi-Fidelity Machine Learning Applied to Steady Fluid Flows
