Two-dimensional array-based datasets are pervasive in a variety of domains. Current approaches for generative modeling have typically been limited to conventional image and performed the pixel domain which does not explicitly capture correlation between pixels. Additionally, these do extend scientific other applications where each element value is continuous fixed range. In this paper, we propo...

We present a protocol for the fully automated construction of quantum mechanical (QM)–classical hybrid models by extending our previously reported approach on self-parametrizing system-focused atomistic (SFAMs) [Brunken, C.; Reiher, M. J. Chem. Theory Comput. 2020, 16, 3, 1646?1665]. In this QM/SFAM approach, size and composition QM region are evaluated in an manner based first principles so th...

This project aims to explore hybrid and automated programming models, including an analysis of their advantages and disadvantages from a programmers perspective, as a step toward answering the question, Will automated programming models someday replace hybrid models as the norm on large-scale and heterogeneous architectures? This paper examines several hybrid and several automated models genera...

A tailored model of a system is the prerequisite for various analysis tasks, such as anomaly detection, fault identification, or quality assurance. This paper deals with algorithmic learning system’s behavior given sample observations. In particular, we consider real-world production plants where learned must capture timing behavior, dependencies between variables, well mode switches—in short: ...