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 awardees will receive an appropriately higher stipend based on the number of years of experience past their PhD.
Awardees who reside more than 50 miles from their host laboratory and remain on tenure for at least six months are eligible for paid relocation to within the vicinity of their host laboratory.
A group health insurance program is available to awardees and their qualifying dependents in the United States.