Researchers at The University of Texas at Arlington have developed a new computational tool that helps scientists pinpoint ...

Abstract: Approximate Bayesian computation is a popular methodology for simulation-based parameter inference in scenarios where the likelihood function is either analytically intractable or ...

Here, we combine active-learning Bayesian optimization (BO) algorithms with quantum chemistry methods to address this challenge. Using cysteine as an example, we show that our procedure is both ...

RAVEN is a flexible and multi-purpose probabilistic risk analysis, validation and uncertainty quantification, parameter optimization, model reduction and data knowledge-discovering framework.

ABSTRACT: We explore the performance of various artificial neural network architectures, including a multilayer perceptron (MLP), Kolmogorov-Arnold network (KAN), LSTM-GRU hybrid recursive neural ...

Benchmark comparisons against the widely used cumulant method for computing 2DES signals are performed on small model systems, as well as the nile red molecule. We highlight the advantages of the ...

In this work, a new information retrieval model based on Bayesian networks is proposed. Its aim is to achieve a good retrieval performance by restricting the set of dependencies between terms to most ...

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