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 ...

SMAC offers a robust and flexible framework for Bayesian Optimization to support users in determining well-performing hyperparameter configurations for their (Machine Learning) algorithms, datasets ...

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

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 ...

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 ...

Abstract: This paper introduces CausalBO, a Python package developed to enhance the applicability and utility of the Causal Bayesian Optimization (CBO) algorithm. The original CBO algorithm, developed ...

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 ...