This repository contains code used to perform acoustic parameter estimation using Bayesian optimization with a Gaussian process surrogate model. The following papers use this code: William Jenkins, ...

Bayesian regression with linear basis function models. Introduction to Bayesian linear regression. Implementation with plain NumPy and scikit-learn. See also PyMC3 implementation. Gaussian processes.

The Nature Index 2026 Research Leaders reveal the leading institutions and countries/territories in the natural sciences, health sciences, applied sciences and social sciences, according to their ...

Article subjects are automatically applied from the ACS Subject Taxonomy and describe the scientific concepts and themes of the article. Developing novel materials drives significant breakthroughs ...

Abstract: Duringthe design of electrical machines, multiple performance objectives need to be considered. Although stochastic optimization algorithms are extensively employed for this purpose, a ...

ProcessOptimizer is a Python package designed to provide easy access to advanced machine learning techniques, specifically Bayesian optimization using, e.g., Gaussian processes. Aimed at ...

Optimization of materials’ performance for specific applications often requires balancing multiple aspects of materials’ functionality. Even for the cases where a generative physical model of material ...

Abstract: In this paper, we propose an efficient Bayesian optimization approach for analog circuit synthesis based on the multi-task Gaussian process model. Instead of building the Gaussian process ...