Adaptive Sampling for Risk-Averse Design and Optimization

Boyan Lazarov | 22-ERD-009

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

R. Bollapragada, C. Karamanli, B. Keith, B. Lazarov, S. Petrides, and J. Wang. “An Adaptive Sampling Augmented Lagrangian Method for Stochastic Optimization with Deterministic Constraints.” Computers and Mathematics with Applications. In press, 2023.

J-U. Chen, B. Lazarov, M. Schmidt, S. Petrides. “A LDG Solver for Steady-state incompressible flow” (Presentation, Summer SLAM! LLNL, Livermore, CA, August, 2023).

T. Linke, M. Schmidt, B. Lazarov, J-P. Delplanque, “Optimization Algorithms: Enhancing Everyday Life and Beyond” (Presentation, Summer SLAM! LLNL, Livermore, CA, August, 2023).

B. Lazarov, B. Keith, S. Petrides. ”Comparison of risk-averse measures for topology optimization” (Presentation, USNCCM17, Albuquerque, NM, July, 2023).

B. Lazarov, J. Wang, and B. Keith. “Risk-averse topology optimization” (Presentation, ASME IMECE 2022, Columbus, OH, October, 2022).