This is achieved via Bayesian Design of Experiments, which helps to efficiently navigate parameter search spaces. It balances exploitation of parameter space regions known to lead to good outcomes and ...

AI agents are increasingly deployed in real-world applications, including systems such as Manus, OpenClaw, and coding agents. Existing research has primarily focused on emph{server-side} efficiency, ...

PyBADS is a Python implementation of the Bayesian Adaptive Direct Search (BADS) algorithm for solving difficult and mildly expensive optimization problems, originally implemented in MATLAB. BADS has ...

In the world of Material Informatics (MI), conventional methods involve tremendous laboratory work or extensive simulations that may not yield the expected results. Our objectives are to contribute to ...

Accurate disaster prediction combined with reliable uncertainty quantification is crucial for timely and effective decision-making in emergency management. However, traditional deep learning methods ...

Optimizing operational conditions for complex biological systems used in life sciences research and biotechnology is an arduous task. Here, we apply a Bayesian Optimization-based iterative framework ...

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 ...

A research group has developed SPACIER, an advanced polymer material design tool that integrates machine learning with molecular simulations. As a proof of concept, the group successfully synthesized ...

Abstract: Reinforcement learning (RL) has revolutionized decision-making across a wide range of domains over the past few decades. Yet, deploying RL policies in real-world scenarios presents the ...

The development of automated high-throughput experimental platforms has enabled fast sampling of high-dimensional decision spaces. To reach target properties efficiently, these platforms are ...