Deep Neural Networks have become the tool of choice for Machine Learning practitioners today. They have been successfully applied for solving a large class of learning problems both in the industry and academia with applications in fields such as Computer Vision, Natural Language Processing, Big data Analytics and Bioinformatics. Increasingly neural networks in general and deep learning in particular is being applied to the physical sciences. Deep learning systems are also being adopted in different engineering disciplines like aerospace, electrical and mechanical engineering. Inspired by the success of these applications, under this project, we plan to study the use of deep learning systems for generating surrogate models of high-fidelity physics-based computational fluid dynamics (CFD) and heat transfer datasets. 

key words

Machine Learning, Surrogate Modeling, Computational Fluid Dynamics, Heat Transfer

Citizenship:  Open to U.S. citizens

Level:  Open to Postdoctoral and Senior applicants