Publications

Multi-fidelity information fusion and resource allocation

Jakeman, John D.; Eldred, Michael S.; Geraci, Gianluca G.; Seidl, Daniel T.; Smith, Thomas M.; Gorodetsky, Alex A.; Pham, Trung P.; Narayan, Akil N.; Zeng, Xiaoshu Z.; Ghanem, Roger G.

This project created and demonstrated a framework for the efficient and accurate prediction of complex systems with only a limited amount of highly trusted data. These next generation computational multi-fidelity tools fuse multiple information sources of varying cost and accuracy to reduce the computational and experimental resources needed for designing and assessing complex multi-physics/scale/component systems. These tools have already been used to substantially improve the computational efficiency of simulation aided modeling activities from assessing thermal battery performance to predicting material deformation. This report summarizes the work carried out during a two year LDRD project. Specifically we present our technical accomplishments; project outputs such as publications, presentations and professional leadership activities; and the project’s legacy.