Gerald Schweiger

Christoph Schlager, Lutz Bornmann, Gerald Schweiger

Multiple organisational affiliations are an increasingly common feature of research systems, yet their implications for organisational performance had received limited systematic attention. We developed a scalable, network-based analytical framework that represents simultaneous researcher affiliations as relational links between organisations and applied it to bibliometric data from Austria. Using harmonised publication and affiliation metadata, we constructed two complementary co-affiliation networks: a complete network capturing all simultaneous affiliations and a temporally filtered network retaining only organisational pairs that recurred over time. Network regression analyses showed that geographical proximity remained an important determinant of co-affiliation formation, with spatial distance consistently reducing shared appointments. Clear sectoral differences emerged beyond geography. Universities formed a dense and persistent core of co-affiliations, whereas ties involving medical institutions, government, non-profit and private-sector organisations were often short-lived and attenuated under temporal filtering. Among crosssector links, co-affiliations between universities and research institutes were notably resilient, indicating a more structurally embedded form of organisational integration. We assessed the effect of concurrent affiliations on organisational citation impact across organisational types using field- and year-normalised indicators. Research institutes and universities consistently exhibited higher citation impact than organisations from other sectors, and persistent co-affiliations were associated with greater and more stable scientific visibility.

Adil Mukhtar, Michael Hadwiger, Franz Wotawa et al.

Reproducibility is a cornerstone of scientific research, enabling independent verification and validation of empirical findings. The topic gained prominence in fields such as psychology and medicine, where concerns about non - replicable results sparked ongoing discussions about research practices. In recent years, the fast-growing field of Machine Learning (ML) has become part of this discourse, as it faces similar concerns about transparency and reliability. Some reproducibility issues in ML research are shared with other fields, such as limited access to data and missing methodological details. In addition, ML introduces specific challenges, including inherent nondeterminism and computational constraints. While reproducibility issues are increasingly recognized by the ML community and its major conferences, less is known about how these challenges manifest in applied disciplines. This paper contributes to closing this gap by analyzing the transparency and reproducibility standards of ML applications in building energy systems. The results indicate that nearly all articles are not reproducible due to insufficient disclosure across key dimensions of reproducibility. 72% of the articles do not specify whether the dataset used is public, proprietary, or commercially available. Only two papers share a link to their code - one of which was broken. Two-thirds of the publications were authored exclusively by academic researchers, yet no significant differences in reproducibility were observed compared to publications with industry-affiliated authors. These findings highlight the need for targeted interventions, including reproducibility guidelines, training for researchers, and policies by journals and conferences that promote transparency and reproducibility.

Johannes Exenberger, Matteo Di Salvo, Thomas Hirsch et al.

Machine learning plays an important role in the operation of current wind energy production systems. One central application is predictive maintenance to increase efficiency and lower electricity costs by reducing downtimes. Integrating physics-based knowledge in neural networks to enforce their physical plausibilty is a promising method to improve current approaches, but incomplete system information often impedes their application in real world scenarios. We describe a simple and efficient way for physics-constrained deep learning-based predictive maintenance for wind turbine gearbox bearings with partial system knowledge. The approach is based on temperature nowcasting constrained by physics, where unknown system coefficients are treated as learnable neural network parameters. Results show improved generalization performance to unseen environments compared to a baseline neural network, which is especially important in low data scenarios often encountered in real-world applications.

Christian Møldrup Legaard, Thomas Schranz, Gerald Schweiger et al.

Dynamical systems see widespread use in natural sciences like physics, biology, chemistry, as well as engineering disciplines such as circuit analysis, computational fluid dynamics, and control. For simple systems, the differential equations governing the dynamics can be derived by applying fundamental physical laws. However, for more complex systems, this approach becomes exceedingly difficult. Data-driven modeling is an alternative paradigm that seeks to learn an approximation of the dynamics of a system using observations of the true system. In recent years, there has been an increased interest in data-driven modeling techniques, in particular neural networks have proven to provide an effective framework for solving a wide range of tasks. This paper provides a survey of the different ways to construct models of dynamical systems using neural networks. In addition to the basic overview, we review the related literature and outline the most significant challenges from numerical simulations that this modeling paradigm must overcome. Based on the reviewed literature and identified challenges, we provide a discussion on promising research areas.