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.

MISSION

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.

OVERVIEW

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
(Turner)

Materials Self-assembly Testbed
(Alexander & Oden)

Integrative Research Thrusts

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

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

Research Sub-Thrusts

Large-scale
Bayesian inference
(Marzouk)

Low-dimensional
& Reduced Modeling
(Willcox)

Large-scale
Bayesian OED
(Ghattas)

Optimal Control Under
Uncertainty
(Biros)

Scientific Machine Learning
(Webster)

Predictive
Multiscale Models
& Inadequacy
(Moser)

Optimal Operator
Design
(Dougherty)

Multifidelity Methods
for OUU
(Gunzburger)

Events & Updates

April 2022
Various AEOLUS team members will be attending and presenting at the SIAM Conference on Uncertainty Quantification (UQ22). Karen Willcox and Youssef Marzouk will be giving plenary talks.

March 2022
The BNL/TAMU team's transfer learning error estimation work supported by AEOLUS has been featured in ACM Tech News as well as on the DOE website.

Karen Willcox is delivering an Invited Keynote Talk "Mathematical and Computational Foundations for Enabling Predictive Digital Twins at Scale" at SupercomputingAsia 2022 (SCA22).

DeWitt is organizing the "ICME Case Studies: Successes and Challenges for Generation, Distribution, and Use of Public/Pre-Existing Materials Datasets" at the 2022 TMS Annual Meeting.

More Events