First autonomous 6D phase-space tomography system at LCLS-II achieves real-time beam reconstructions every 5-10 minutes via ML control and generative analysis.
Unexpected improvements to expected improvement for bayesian optimization.arXiv preprint arXiv:2310.20708, 2023
5 Pith papers cite this work, alongside 46 external citations. Polarity classification is still indexing.
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representative citing papers
Data-driven design of 12-fold quasicrystal nanomechanical resonators achieves Q_m of approximately 10^7 and force sensitivity of 26.4 aN per square root Hz.
A multi-source extension of constrained Max-value Entropy Search for Bayesian optimization incorporates auxiliary data sources to improve early exploration and performance under constraints even with weak correlations.
Multi-objective Bayesian optimization of a double-layer TNSA target in 80 EPOCH simulations identifies Pareto-optimal parameters for 64-71 MeV proton peaks in 2D, with a 3D verification case showing 34.1 MeV peak and narrower 7% bandwidth.
Synthetic simulations show noise hurts needle-in-haystack optimization far more than smooth landscapes with local optima, and prior domain knowledge of noise and structure is needed for effective BO in materials research.
citing papers explorer
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Autonomous operation of the DIAG0 diagnostic line for 6D phase-space monitoring at LCLS-II
First autonomous 6D phase-space tomography system at LCLS-II achieves real-time beam reconstructions every 5-10 minutes via ML control and generative analysis.
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Quasicrystal Architected Nanomechanical Resonators via Data-Driven Design
Data-driven design of 12-fold quasicrystal nanomechanical resonators achieves Q_m of approximately 10^7 and force sensitivity of 26.4 aN per square root Hz.
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Constrained Bayesian Optimisation with Multiple Information Sources
A multi-source extension of constrained Max-value Entropy Search for Bayesian optimization incorporates auxiliary data sources to improve early exploration and performance under constraints even with weak correlations.
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Multi-objective Bayesian optimisation of a double-layer target for quasi-monoenergetic TNSA protons
Multi-objective Bayesian optimization of a double-layer TNSA target in 80 EPOCH simulations identifies Pareto-optimal parameters for 64-71 MeV proton peaks in 2D, with a 3D verification case showing 34.1 MeV peak and narrower 7% bandwidth.
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Multi-Variable Batch Bayesian Optimization in Materials Research: Synthetic Data Analysis of Noise Sensitivity and Problem Landscape Effects
Synthetic simulations show noise hurts needle-in-haystack optimization far more than smooth landscapes with local optima, and prior domain knowledge of noise and structure is needed for effective BO in materials research.