Machine Learning for Computational Fluid Dynamics and Heat Transfer
Munitions Directorate, RW/Weapons Engagement
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.
Experience Supplement: Postdoctoral and Senior Associates will receive an appropriately higher stipend based on the number of years of experience past their PhD.