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BOOTS: Bayesian Optimization for Optimal Test Selection
BOOTS is a package for optimal selection of candidate operating points in large-scale manufacturing applications. It is designed to optimally select operating points to maximize predicted values of product quality and resource efficiency according to a data-driven model. The package is based on the use of a multi-input multi-output (MIMO) Gaussian process model to describe the relationships between inputs (operating points) and outputs (product quality and resource efficiency). This package does not provide any site- or process-specific information.
Multi-fidelity ML/UQ and Bayesian Optimization for Materials Design: Application to Ternary Random Alloys.
Abstract not provided.
Solving inverse problems for process-structure linkages using asynchronous parallel Bayesian optimization.
Abstract not provided.
Bayesian Optimal Experimental Design for Chemical Rate Constant Measurement using Mass Spectrometry.
Abstract not provided.
Bayesian Optimal Experimental Design for Seismic Monitoring.
Abstract not provided.
Scalable3-BO: Big Data meets HPC - A scalable parallel high-dimensional Bayesian optimization framework on supercomputers.
Abstract not provided.
Gaussian process and Bayesian optimization - Bridging the gap between theory and practice in materials science.
Abstract not provided.
Complex-Valued Signal Denoising and Bayesian Optimization for Detection of Synthetic Opioids [Slides]
Overseas manufacturers ship synthetic opioids into the United States through international mail. Synthetic opioids are largely responsible for the overdose crisis in the United States. Nuclear Quadrupole Resonance (NQR) spectroscopy is a chemical analysis technique used for detection. The ultimate goal is to develop a technology capable of detecting the presence of synthetic opioids in unopened packages using NQR spectroscopy.
Multi-fidelity Gaussian process and Bayesian optimization for materials design: Application to ternary random alloys.
Abstract not provided.
Machine learning and Bayesian optimization for pulse shaping on a linear induction accelerator
Conference: 14th International Particle Accelerator Conference Date: May 7th-12th, 2023 Location: Lido di Venezia, Italy Presenter: Evan R Scott
Versatile Gaussian process and Bayesian optimization for multiscale computational materials science applications.
Abstract not provided.
Exploring Bayesian Optimal Experimental Designs for High-dimensional Combustion Systems.
Abstract not provided.
Bayesian Optimal Experimental Design for Photoionization Mass Spectrometry Experiments.
Abstract not provided.
Enabling the fourth-paradigm of materials science through versatile Gaussian process and Bayesian optimization.
Abstract not provided.
Versatile Gaussian Process and Bayesian Optimization for Multiscale Computational Materials Science Applications.
Abstract not provided.
Utilizing Bayesian Optimization for Efficient Dispersion Curve Feature Acquisition [Slides]
Locate features and densely sample near those features. This helps us calculate T 1 which is the magnitude of the deviation from the otherwise smooth monotonically increasing function. Get a generally good idea of the rest of the curve. Potentially incorporate the higher cost of sampling at higher frequencies and the fact that the features at higher frequencies are more valuable to identify. Create a useful physics informed mean function.