41 citations · 49 across the 8 of their papers we have counts for
9 papers
Domain Knowledge Guided Bayesian Optimization For Autonomous Alignment Of Complex Scientific Instruments
Aashwin Mishra, Matt Seaberg, Ryan Roussel +2
Bayesian Optimization (BO) is a powerful tool for optimizing complex non-linear systems. However, its performance degrades in high-dimensional problems with tightly coupled paramet…
Deep Learning Enabled Nanoscale X-ray Photoemission Electron Microscopy (nanoXPEEM)
Aashwin Mishra, Daniel Ratner, Quynh Nguyen
Understanding and manipulating two-dimensional materials for real-world applications remains challenging due to a lack of effective and high-throughput characterization techniques.…
Efficient Dynamic and Momentum Aperture Optimization for Lattice Design Using Multipoint Bayesian Algorithm Execution
Z. Zhang, I. Agapov, S. Gasiorowski +4
We demonstrate that multipoint Bayesian algorithm execution can overcome fundamental computational challenges in storage ring design optimization. Dynamic (DA) and momentum (MA) op…
Data Driven Drift Correction For Complex Optical Systems
Aashwin Mishra, Matt Seaberg, Ryan Roussel +4
To exploit the thousand-fold increase in spectral brightness of modern light sources, increasingly intricate experiments are being conducted that demand extremely precise beam traj…
A Start To End Machine Learning Approach To Maximize Scientific Throughput From The LCLS-II-HE
Aashwin Mishra, Matt Seaberg, Ryan Roussel +5
With the increasing brightness of Light sources, including the Diffraction-Limited brightness upgrade of APS and the high-repetition-rate upgrade of LCLS, the proposed experiments…
Leveraging Prior Mean Models for Faster Bayesian Optimization of Particle Accelerators
Tobias Boltz, Jose L. Martinez, Connie Xu +7
Tuning particle accelerators is a challenging and time-consuming task that can be automated and carried out efficiently using suitable optimization algorithms, such as model-based…
Targeted materials discovery using Bayesian algorithm execution
Sathya Chitturi, Akash Ramdas, Yue Wu +8
Rapid discovery and synthesis of new materials requires intelligent data acquisition strategies to navigate large design spaces. A popular strategy is Bayesian optimization, which…
Bayesian Optimization Algorithms for Accelerator Physics
Ryan Roussel, Auralee L. Edelen, Tobias Boltz +23
Accelerator physics relies on numerical algorithms to solve optimization problems in online accelerator control and tasks such as experimental design and model calibration in simul…
Physics-informed Gaussian Process for Online Optimization of Particle Accelerators
Adi Hanuka, X. Huang, J. Shtalenkova +5
High-dimensional optimization is a critical challenge for operating large-scale scientific facilities. We apply a physics-informed Gaussian process (GP) optimizer to tune a complex…