arXiv subjects

Tyler H. Chang

Publications and source records attributed to Tyler H. Chang.

4 recordsLinked to original sources

arXiv2025

Designing a Framework for Solving Multiobjective Simulation Optimization Problems

Multiobjective simulation optimization (MOSO) problems are optimization problems with multiple conflicting objectives, where evaluation of at least one of the objectives depends on a black-box numerical code or real-world experiment, which we refer to as a simulation. While an extensive body of research is dedicated to developing new algorithms and methods for solving these and related problems, it is challenging and time consuming to integrate these techniques into real world production-ready solvers. This is partly due to the diversity and complexity of modern state-of-the-art MOSO algorithms and methods and partly due to the complexity and specificity of many real-world problems and their corresponding computing environments. The complexity of this problem is only compounded when introducing potentially complex and/or domain-specific surrogate modeling techniques, problem formulations, design spaces, and data acquisition functions. This paper carefully surveys the current state-of-the-art in MOSO algorithms, techniques, and solvers; as well as problem types and computational environments where MOSO is commonly applied. We then present several key challenges in the design of a Parallel Multiobjective Simulation Optimization framework (ParMOO) and how they have been addressed. Finally, we provide two case studies demonstrating how customized ParMOO solvers can be quickly built and deployed to solve real-world MOSO problems.

math.OC↗

arXiv2023

An Integrated Multi-Physics Optimization Framework for Particle Accelerator Design

The overarching goal of beamline design is to achieve a high brightness electron beam from the beamline. Traditional beamline design studies involved separate optimizations of radio-frequency cavities, magnets, and beam dynamics using different codes and pursuing various intermediate objectives. In this work, we present a novel unified global optimization framework that integrates multiple physics modules for beamline design as simulation functions for a two-stage global optimization solver.

physics.acc-ph↗

arXiv2023

A framework for fully autonomous design of materials via multiobjective optimization and active learning: challenges and next steps

In order to deploy machine learning in a real-world self-driving laboratory where data acquisition is costly and there are multiple competing design criteria, systems need to be able to intelligently sample while balancing performance trade-offs and constraints. For these reasons, we present an active learning process based on multiobjective black-box optimization with continuously updated machine learning models. This workflow is built on open-source technologies for real-time data streaming and modular multiobjective optimization software development. We demonstrate a proof of concept for this workflow through the autonomous operation of a continuous-flow chemistry laboratory, which identifies ideal manufacturing conditions for the electrolyte 2,2,2-trifluoroethyl methyl carbonate.

cs.LG↗

arXiv2022

Comparison of Multiobjective Optimization Methods for the LCLS-II Photoinjector

Particle accelerators are among some of the largest science experiments in the world and can consist of thousands of components with a wide variety of input ranges. These systems can easily become unwieldy optimization problems during design and operations studies. Starting in the early 2000s, searching for better beam dynamics configurations became synonymous with heuristic optimization methods in the accelerator physics community. Genetic algorithms and particle swarm optimization are currently the most widely used. These algorithms can take thousands of simulation evaluations to find optimal solutions for one machine prototype. For large facilities such as the Linac Coherent Light Source (LCLS) and others, this equates to a limited exploration of many possible design configurations. In this paper, the LCLS-II photoinjector is optimized with three optimization algorithms. All optimizations were started from both a uniform random and Latin hypercube sample. In all cases, the optimizations started from Latin hypercube samples outperformed optimizations started from uniform samples. All three algorithms were able to optimize the photoinjector, with the model-based methods approximating the Pareto front in fewer simulation evaluations. This work, in combination with previous optimization observations, indicates objective penalties have a strong impact on the efficiency of such methods. In general, we recommend heuristic methods for initial optimizations and model-based methods when information about the objective space is available.

physics.acc-ph↗