The package contains functions to compute option-implied moments and characteristics from implied volatility surface data. The computations are based on the out-the-money (OTM) implied volatilities, ...

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 ...

In this work, we propose to represent chemical environments as vectorized objects which can be used as input for machine learning (ML) properties of atomistic systems. The proposed method efficiently ...

To get a better understanding of electromagnetics and free myself from the commercial EM software, I’ve been trying to make my own antenna simulator in the format Notebook using Python [1]. The ...

This package delivers a scikit-learn compatible Python 3 package for sundry state-of-the art multivariate statistical methods, with a focus on dimension reduction.

In recent years, we have witnessed an unimaginable growth in data production. From personalized medicine to finance, datasets characterized by a large number of features are ubiquitous in modern data ...

Computational methods in protein engineering often require encoding amino acid sequences, i.e., converting them into numeric arrays. Physicochemical properties are a typical choice to define encoders, ...

Uncertainties are widespread in the optimization of process systems, such as uncertainties in process technologies, prices, and customer demands. In this paper, we review the basic concepts and recent ...

Atomic neural networks (ANNs) constitute a class of machine learning methods for predicting potential energy surfaces and physicochemical properties of molecules and materials. Despite many successes, ...