Development of machine learning tools for aerospace design: wind tunnel investigations on a speed bump model
Author:
Affiliation:
1. California Institute of Technology
2. Boeing Research & Technology
Publisher
American Institute of Aeronautics and Astronautics
Link
https://arc.aiaa.org/doi/pdf/10.2514/6.2024-2834
Reference26 articles.
1. Physics-informed neural networks: A deep learning framework for solving forward and inverse problems involving nonlinear partial differential equations
2. Physics-informed neural networks for high-speed flows
3. Uncovering turbulent plasma dynamics via deep learning from partial observations
4. NSFnets (Navier-Stokes flow nets): Physics-informed neural networks for the incompressible Navier-Stokes equations
5. Scientific Machine Learning Through Physics–Informed Neural Networks: Where we are and What’s Next
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