BASIL: Bayesian Application for Scientific Iteration and Learning

2026-06-19

AI Summary

This work presents a general-purpose desktop platform based on Bayesian optimization to address both single- and multi-objective optimization problems commonly encountered in scientific experimentation and industrial processes. The platform features a graphical user interface that accepts user-defined input parameters, optimization objectives, and historical data, and integrates surrogate models—such as Gaussian processes—with customizable acquisition functions tailored for single- or multi-objective settings to automatically guide iterative experimentation. Its key innovation lies in encapsulating advanced Bayesian optimization techniques into an accessible, configurable, and interactive tool that significantly reduces the number of required experiments while efficiently converging toward optimal solutions, thereby enhancing experimental efficiency and the intelligence of decision-making processes.

Abstract

We introduce BASIL, a user-friendly desktop application for process optimization. BASIL employs a Bayesian approach, incorporating special acquisition functions that can be used to solve both single and multi-objective optimization problems. It provides a graphical interface that enables users to input their experimental parameters, optimization objectives, and legacy data. This is then used to build surrogate models, which are coupled with acquisition functions to guide and optimize a process towards a desired objective. To facilitate model building, BASIL provides a variety of predefined surrogate model templates. BASIL can be used to optimize any arbitrary experiment or process with known, user-defined input variables, optimization objectives, and defined output.

Problem

• process optimization
• Bayesian optimization
• multi-objective optimization
• surrogate modeling
• scientific experimentation

Innovation

• Bayesian optimization
• acquisition function
• surrogate modeling
• multi-objective optimization
• process optimization