4 citations · 9 across the 8 of their papers we have counts for
8 papers
Leveraging Interpolation Models and Error Bounds for Verifiable Scientific Machine Learning
Tyler Chang, Andrew Gillette, Romit Maulik
Effective verification and validation techniques for modern scientific machine learning workflows are challenging to devise. Statistical methods are abundant and easily deployed, b…
An Integrated Multi-Physics Optimization Framework for Particle Accelerator Design
Gongxiaohui Chen, Tyler H. Chang, John Power +1
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 radi…
SF-SFD: Stochastic Optimization of Fourier Coefficients to Generate Space-Filling Designs
Manisha Garg, Tyler Chang, Krishnan Raghavan
Due to the curse of dimensionality, it is often prohibitively expensive to generate deterministic space-filling designs. On the other hand, when using na{ï}ve uniform random sampli…
A framework for fully autonomous design of materials via multiobjective optimization and active learning: challenges and next steps
Tyler H. Chang, Jakob R. Elias, Stefan M. Wild +2
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 ab…
Designing a Framework for Solving Multiobjective Simulation Optimization Problems
Tyler H. Chang, Stefan M. Wild
Multiobjective simulation optimization (MOSO) problems are optimization problems with multiple conflicting objectives, where evaluation of at least one of the objectives depends on…
Comparison of Multiobjective Optimization Methods for the LCLS-II Photoinjector
Nicole Neveu, Tyler H. Chang, Paris Franz +2
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 c…
Design Strategies and Approximation Methods for High-Performance Computing Variability Management
Yueyao Wang, Li Xu, Yili Hong +6
Performance variability management is an active research area in high-performance computing (HPC). We focus on input/output (I/O) variability. To study the performance variability,…
Prediction of High-Performance Computing Input/Output Variability and Its Application to Optimization for System Configurations
Li Xu, Thomas Lux, Tyler Chang +6
Performance variability is an important measure for a reliable high performance computing (HPC) system. Performance variability is affected by complicated interactions between nume…