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Graz University of Technology

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Research library305linked papers
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Selected work

Representative Papers

Toward generative machine learning for boosting ensembles of climate simulations

Feb 06, 2026

This study addresses the challenge of quantifying uncertainty arising from internal climate variability, which is hindered by the prohibitive computational cost of running high-resolution, large-ensemble climate simulations. For the first time, a conditional variational autoencoder (cVAE) is applied to CMIP6 CanESM5 monthly-scale data, enhanced with an output noise injection mechanism to better capture multiscale climate variability. The proposed method generates physically consistent synthetic ensembles of arbitrary size from limited samples, accurately reproducing realistic teleconnection patterns and both low- and high-order statistical features—including extremes—even under climate conditions not present in the training data. This approach significantly improves the reliability of uncertainty assessments for both historical and future climate scenarios, while maintaining computational efficiency and mathematical interpretability.

1 citationsRead paper

Can Platform Design Encourage Curiosity? Evidence from an Independent Social Media Experiment

Jan 22, 2026

Social media platforms are often criticized for amplifying antisocial behaviors and lacking effective mechanisms to foster prosocial tendencies such as curiosity. This study addresses this gap by constructing an independent experimental platform and conducting a randomized controlled trial with 2,282 U.S. adults in a highly controlled environment. Using AI-driven virtual users to simulate authentic social interactions, the research systematically manipulated platform social norms and interface design. Findings demonstrate that curiosity-inducing interventions significantly increased users’ question-asking frequency and textual markers of curiosity while reducing toxic language. Although these interventions decreased generalized engagement metrics—such as likes and comments—they did not adversely affect subjective user experience or time spent creating content. The study thus provides causal evidence and a practical design pathway for promoting prosocial behavior on digital platforms.

1 citationsRead paper

Thermal Model Calibration of a Squirrel-Cage Induction Machine

Sep 01, 2024International Conference on Electrical Machines

Early identification of thermal hotspots and excessive safety margins remain key challenges in induction motor thermal design. Method: This paper proposes a two-dimensional thermal model calibration approach based on inverse field problems, jointly estimating material thermal properties and equivalent parameters for three-dimensional (3D) thermal effects using only measured temperature data—without requiring prior knowledge of detailed 3D geometry. Contribution/Results: The method is the first systematic application of inverse modeling to induction motor thermal analysis. Integrated with parametric sensitivity analysis and validated against both synthetic and experimental data, it significantly reduces thermal prediction error in both academic benchmarks and real-world motors. The approach enables accurate localization of thermal weak points at early design stages, thereby facilitating reduction of unnecessary safety margins, enhancement of power density, and improvement of overall reliability.

1 citationsRead paper

pyCFS-data: Data Processing Framework in Python for openCFS

May 06, 2024arXiv.org

Existing open-source multiphysics simulation tools (e.g., openCFS) lack efficient and flexible Python-based data processing capabilities for coupled-field problems such as aeroacoustics, resulting in fragmented pre- and post-processing ecosystems. To address this, we propose the first lightweight, extensible, Python-native data framework tailored for openCFS. It unifies parsing of openCFS’s native XML configuration and finite-element data formats, and integrates HDF5 I/O, NumPy/Pandas-backed computation, and object-oriented design principles. The framework bridges the cross-language gap between openCFS’s C++ core and user-facing data analysis, enabling modular, pipeline-driven preprocessing and postprocessing. It supports batched mesh analysis, real-time visualization, and machine-learning-ready data export. Evaluated across multiple aeroacoustic case studies, the framework demonstrates robustness, usability, and significant efficiency gains in end-to-end workflows.

1 citationsRead paper
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