TY - CONF A1 - Weichert, D. A1 - Kister, Alexander A1 - Houben, S. A1 - Ernis, G. A1 - Wrobel, S. T1 - Robustness in Fatigue Strength Estimation N2 - Fatigue strength estimation is a costly manual material characterization process in which state-of-the-art approaches follow a standardized experiment and analysis procedure. In this paper, we examine a modular, Machine Learning-based approach for fatigue strength estimation that is likely to reduce the number of experiments and, thus, the overall experimental costs. Despite its high potential, deployment of a new approach in a real-life lab requires more than the theoretical definition and simulation. Therefore, we study the robustness of the approach against misspecification of the prior and discretization of the specified loads. We identify its applicability and its advantageous behavior over the state-of-the-art methods, potentially reducing the number of costly experiment. T2 - 2nd Annual AAAI Workshop on AI to Accelerate Science and Engineering CY - Washington DC, USA DA - 07.02.2023 KW - Active Learning KW - Gaussian Process KW - Fatigue Strength PY - 2022 AN - OPUS4-56684 LA - eng AD - Bundesanstalt fuer Materialforschung und -pruefung (BAM), Berlin, Germany ER -