AEOLUS: Advances in Experimental Design, Optimization and Learning for Uncertain Complex Systems is a U.S. Department of Energy Mathematical Multifaceted Integrated Capabilities Center (MMICC) involving researchers from Brookhaven National Laboratory, Massachusetts Institute of Technology, Oak Ridge National Laboratory, Texas A&M University, and University of Texas at Austin.


The AEOLUS Center dedicated to developing a unified optimization-under-uncertainty framework for (1) learning predictive models from data and (2) optimizing experiments, processes, and designs, all in the context of complex, uncertain energy systems. The AEOLUS center will address the critical need for a principled, rigorous, scalable, and structure-exploiting capability for exploring parameter and decision spaces of complex forward simulation models.


The AEOLUS team conducts research within eight research sub-thrusts, organized into two integrative research thrusts, and driven by DOE scientific applications.

Driving Scientific Application Area: Advanced Manufacturing & Materials

Additive Manufacturing Testbed

Materials Self-assembly Testbed
(Alexander & Oden)

Integrative Research Thrusts

predictive models via Bayesian inference & optimization
(Webster & Willcox)

experiments, processes, & designs under uncertainty
(Alexander & Ghattas)

Research Sub-Thrusts

Bayesian inference

& Reduced Modeling

Bayesian OED

Optimal Control Under

Scientific Machine Learning

Multiscale Models
& Inadequacy

Optimal Operator

Multifidelity Methods
for OUU

Events & Updates

November 2020
Stephen DeWitt, Bala Radhakrishnan, Yuanxun Bao, Yigong Qin, George Biros, Jean-Luc Fattebert, and John Turner, “Phase Field Modeling of AM Solidification Microstructure with Algorithmic Feature Extraction to Facilitate Reduced Order Model Development”, MS&T 2020 (Virtual)

October 2020
Frank Alexander, Omar Ghattas and Karen Willcox are part of the Planning Committee for the New York Scientific Data Summit 2020.

August 2020
Willcox, K. - “Toward predictive digital twins: From physics-based modeling to scientific machine learning.“ Opening Keynote, ASME Design Automation Conference, August 2020.

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