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, ...
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 ...
pyforce is a Python package implementing Data-Driven Reduced Order Modelling (DDROM) techniques for applications to multi-physics problems, mainly set in the Nuclear Engineering world. The package is ...
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, ...
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