Machine learning and deep learning for CFD

Apr 1, 2021
github

Computational fluid dynamics (CFD) represents an attractive tool for estimating wind pressures and wind loads on high-rise buildings. The CFD analyses can be conducted either by low-fidelity simulations (RANS) or by high-fidelity ones (LES). The low-fidelity model can efficiently estimate wind pressures over a large range of wind directions, but it generally lacks accuracy. On the other hand, the high-fidelity model generally exhibits satisfactory accuracy, yet, the high computational cost can limit the number of approaching wind angles that can be considered. In order to take advantage of the main benefits of these two CFD approaches, a multi-fidelity machine learning framework is investigated that aims to ensure the simulation accuracy while maintaining the computational efficiency.

Machine Learning Multi-fidelity Model Order Reduction Numerical Mathematics Computational Fluid Dynamics RANS and LES
Onkar Jadhav
Onkar Jadhav
Research Fellow

Applied Machine Learning Scientist specializing in the research, optimization, and machine learning. As a researcher, I bring deep theoretical and practical understanding of Transformer architectures and state-of-the-art deep learning methods. I am proficient in developing robust ML pipelines and translating cutting-edge research into effective product features.

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