Nature

Honegumi: An Interface for Accelerating the Adoption of Bayesian Optimization in the Experimental Sciences

We are providing an unedited version of this manuscript to give early access to its findings. Before final publication, the manuscript will undergo further editing. Please note there may be errors ...
leewayhertz

Bayesian networks in AI: Role in machine learning, example, types and applications

Imagine a scenario where a team of doctors faces a perplexing medical puzzle. A patient shows a range of symptoms, each pointing to multiple possible diseases. How can they navigate this diagnostic ...
IEEE

Efficient Counterexample Generation for Control Systems Using Multi-Fidelity Bayesian Optimization

Abstract: Testing controllers in safety-critical systems is vital for ensuring their safety and preventing catastrophic failures. In this paper, we address the falsification problem within closed-loop ...
note

[Udemy] The Ultimate Learning Method a Researcher Found After Struggling to Stick with Python

I have had the experience of letting dozens of programming books pile up unread. Haven't you all had that experience too? I would think, "I'll read it this week," but as soon as experiments piled up, ...
GitHub

PyBADS: Bayesian Adaptive Direct Search in Python

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 ...
Scientific Research Publishing

A Proposal of Genuine Computing Methods in Materials Informatics ()

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 ...
GitHub

Bayesian Optimization & Analysis of Hyperparameters

This repository provides simple examples of how to construct a configuration space using the ConfigSpace package, how to use BOHB with minimal efforts and how to run CAVE to generate a comprehensive ...
Frontiers

Enhancing disaster prediction with Bayesian deep learning: a robust approach for uncertainty estimation

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

Pruning Bayesian networks for computationally tractable multi-model calibration

Anomaly response in aerospace systems increasingly relies on multi-model analysis in digital twins to replicate the system’s behaviors and inform decisions. However, computer model calibration methods ...
C&EN

ProcessOptimizer, an Open-Source Python Package for Easy Optimization of Real-World Processes Using Bayesian Optimization: Showcase of Features and Example of Use

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 ...
Phys.org

Automated polymer design tool integrates machine learning and molecular simulations

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 ...