Adaptive Sampling for Risk-Averse Design and Optimization

Executive Summary

We will develop a new approach to address uncertainty during the engineering design process to enable practical, computationally feasible, and scalable algorithmic solutions to reduce overall risk and optimize performance. These methods can be used to improve the design and production of new materials and additively manufactured parts in support of national security missions.

Publications, Presentations, and Patents

Tobias Duswald, Boyan Lazarov, Brendan Keith, and Socratis Petrides. “Gaussian Random Fields.” Summer SLAM! LLNL. Aug. 2022.

Bollapragada, R., C. Karamanli, B. Keith, B. Lazarov, S. Petrides, and J. Wang. “Adaptive Sampling for the Augmented Lagrangian Method.” ICCOPT 2022, Bethlehem, PA. July, 2202.

Bollapragada, Raighu, David Newton, Raghu Pasupathy and Nung Kwan Yip. “Retrospective Approximation for Smooth Stochastic Optimization.” INFORMS Optimization Society Conference, Greenville, South Carolina. March 2022.

Bollapragada, Raghu, Albert S. Berahas, and Baoyu Zhou. “Adaptive Sampling Stochastic Sequential QuadraticProgramming.” International Conference on Continuous Optimization ICCOPT 2022 Bethlehem, PA. July 2022.