Most machine learning algorithms are configured by a set of hyperparameters whose values
must be carefully chosen and which often considerably impact performance. To avoid a time …

This report describes the physics case, the resulting detector requirements, and the evolving
detector concepts for the experimental program at the Electron-Ion Collider (EIC). The EIC …

Algorithm parameters, in particular hyperparameters of machine learning algorithms, can
substantially impact their performance. To support users in determining well-performing …

Reaction optimization is fundamental to synthetic chemistry, from optimizing the yield of
industrial processes to selecting conditions for the preparation of medicinal candidates …

This paper presents the results and insights from the black-box optimization (BBO)
challenge at NeurIPS2020 which ran from July–October, 2020. The challenge emphasized …

Abstract Machine learning (ML) has been increasingly used to aid aerodynamic shape
optimization (ASO), thanks to the availability of aerodynamic data and continued …

Structural Reliability analysis (SRA) is one of the prominent fields in civil and mechanical
engineering. However, an accurate SRA in most cases deals with complex and costly …

Advances in machine learning methods provide tools that have broad applicability in
scientific research. These techniques are being applied across the diversity of nuclear …

Since deep neural networks were developed, they have made huge contributions to
everyday lives. Machine learning provides more rational advice than humans are capable of …

One of the most exciting tools that have entered the material science toolbox in recent years
is machine learning. This collection of statistical methods has already proved to be capable …