Scalable Environment for Quantification of Uncertainty and Optimization in Industrial Applications: (U-M PI: K. Duraisamy).
This project develops an integrated plan for performing uncertainty quantification
(UQ) and design under uncertainty (DUU) that aggressively pursues new frontiers in scale
and complexity. In particular, this project will create advancements in scalable forward and inverse UQ algorithms and the rigorous quantification of model inadequacy using data. This project provides a foundation for the development of generalized stochastic design approaches that address the robustness and reliability of complex multi-disciplinary systems. This is a collaborative effort with Stanford University, Colorado School of Mines and Sandia National Laboratories.
Funded by DARPA
Framework for Turbulence Modeling using Big Data (PI: K. Duraisamy)
Developing the science behind data-driven turbulence modeling and demonstrate the utility of large-scale data-driven techniques in turbulence modeling.It involves the development of domain-specific learning techniques suited for the representation of turbulence and its modeling, the establishment of a trusted ensemble of data for the creation and validation of new models, and the deployment of these models in complex aerospace problems. This is a collaborative effort between the University of Michigan, Stanford University, Iowa State and Pivotal Inc., consulting with Boeing Commerical Airplanes and interacting with NASA Langley Research Center.
Funded by the LEARN (Leading Edge Aeronautics Research for NASA) program, through the NASA Aeronautics Research Institute (NARI)
Formalisms and Tools for Data-driven Turbulence Modeling (PI: K. Duraisamy)
Devising rigorous mathematical techniques that utilize large databases obtained by simulations to develop predictive models of turbulent flow. The tools are of general applicability for data-driven modeling in all areas of science and engineering.
Funded by NSF
Integrated computational framework for designing dynamically controlled alloy-oxide heterostructures (PIs: E. Marquis & K. Garikipati)
This project will develop an openly distributable framework that rigorously integrates theory, experiment and computation to predict and elucidate the evolution of complex materials heterostructures. A central challenge is linking the electronic structure of the constituent chemistries of a complex materials system to its behavior at technologically relevant length and time scales.
Funded by NSF