@incollection{decke2023examination, abstract = { In the field of design optimization, numerical equation solvers, commonly used for finite element analysis and computational fluid dy- namics, impose significant computational demands. The iterative nature of design optimization, involving numerous simulations, magnifies re- source requirements in terms of time, energy, costs, and greenhouse gas emissions. Accelerating this process has been a long-standing research challenge, driven by the potential for resource savings and the ability to tackle increasingly complex problems. Recently, organic computing methods have gained prominence as promising approaches to address this challenge. This study aims to bridge the gap by integrating tech- niques from deep learning, active learning, and generative learning into the field of design optimization. The goal is to accelerate the design op- timization process while addressing specific challenges such as exploring large and complex design spaces and evaluating the advantages and con- straints of these methods. This research has the potential to significantly impact the industrial use of design optimization by providing faster and more efficient tools.}, author = {Decke, Jens}, booktitle = {Organic Computing -- Doctoral Dissertation Colloquium 2023}, editor = {Tomforde, Sven and Krupitzer, Christian}, interhash = {7d9d4124b3086a01e052ac3d3b8563dd}, intrahash = {a8bae3d276fcc8c617284a15017b0f58}, pages = {41--54}, publisher = {kassel university press}, title = {An Examination of Organic Computing Strategies in Design Optimization}, year = 2023 } @incollection{botache2023machine, abstract = {The increasing environmental pollution and rising global energy demands require the rapid development of novel and sustainable traction machines from urban transport up to large-scale transport vehicles. The optimisation of the development cycle of electrical traction machines plays a vital role in sustainable mobility. Still, developers have to face the difficulty of analysing time-consuming multiphysics simulations to assess the performance of many prototype variants. Accelerating this process involves implementing machine learning-supported strategies that can effectively acquire results and predictions about the performance and efficiency of prototypes in an iterative optimisation process and detect possible issues during the experimental procedure. The following research project streamlines the development cycle by identifying and presenting multiple strategies for applying and evaluating machine learning and deep learning techniques in different stages and areas in the development cycle including self-optimisation and self-adaptation capabilities. By implementing these strategies, the optimisation process can be further improved and become more efficient and robust, reducing the time and cost required for testing and validation. Therefore, this proposal addresses two application areas. The first area consists of the supported machine learning motor performance prediction at the topology design and optimisation stage of structural components. The second area corresponds to the machine learning-supported experimental evaluation process of prototypes at desired test benches. }, author = {Botache, Diego}, booktitle = {Organic Computing -- Doctoral Dissertation Colloquium 2023}, editor = {Tomforde, Sven and Krupitzer, Christian}, interhash = {bfa9ddfeab2d39dca2375127dc39d1cb}, intrahash = {f68d78692b7048293d759eb4527731be}, pages = {14--26}, publisher = {kassel university press}, title = {Machine Learning Supported Optimisation and Experimental Evaluation of Electrical Motors for Small Urban Passenger Vehicles}, year = 2023 } @article{botache2024enhancing, abstract = {This paper presents a methodological framework for training, self-optimising, and self-organising surrogate models to approximate and speed up multiobjective optimisation of technical systems based on multiphysics simulations. At the hand of two real-world datasets, we illustrate that surrogate models can be trained on relatively small amounts of data to approximate the underlying simulations accurately. Including explainable AI techniques allow for highlighting feature relevancy or dependencies and supporting the possible extension of the used datasets. One of the datasets was created for this paper and is made publicly available for the broader scientific community. Extensive experiments combine four machine learning and deep learning algorithms with an evolutionary optimisation algorithm. The performance of the combined training and optimisation pipeline is evaluated by verifying the generated Pareto-optimal results using the ground truth simulations. The results from our pipeline and a comprehensive evaluation strategy show the potential for efficiently acquiring solution candidates in multiobjective optimisation tasks by reducing the number of simulations and conserving a higher prediction accuracy, i.e., with a MAPE score under 5% for one of the presented use cases.}, archiveprefix = {arXiv}, author = {Botache, Diego and Decke, Jens and Ripken, Winfried and Dornipati, Abhinay and Götz-Hahn, Franz and Ayeb, Mohamed and Sick, Bernhard}, eid = {arXiv:2309.13179v2}, eprint = {2309.13179v2}, interhash = {a934e8d2ece9201cf5998ffcea84f757}, intrahash = {730ae1996394beab35bc8f865eadaf67}, journal = {arXiv e-prints}, pages = {arXiv:2309.13179v2}, primaryclass = {cs.LG}, title = {Enhancing Multi-Objective Optimization through Machine Learning-Supported Multiphysics Simulation}, url = {https://arxiv.org/abs/2309.13179}, year = 2024 } @inproceedings{decke2023dado, abstract = {In this work, we apply deep active learning to the field of design optimization to reduce the number of computationally expensive numerical simulations widely used in industry and engineering. We are interested in optimizing the design of structural components, where a set of parameters describes the shape. If we can predict the performance based on these parameters and consider only the promising candidates for simulation, there is an enormous potential for saving computing power. We present two query strategies for self-optimization to reduce the computational cost in multi-objective design optimization problems. Our proposed methodology provides an intuitive approach that is easy to apply, offers significant improvements over random sampling, and circumvents the need for uncertainty estimation. We evaluate our strategies on a large dataset from the domain of fluid dynamics and introduce two new evaluation metrics to determine the model's performance. Findings from our evaluation highlights the effectiveness of our query strategies in accelerating design optimization. Furthermore, the introduced method is easily transferable to other self-optimization problems in industry and engineering.}, author = {Decke, Jens and Gruhl, Christian and Rauch, Lukas and Sick, Bernhard}, booktitle = {International Conference on Machine Learning and Applications (ICMLA)}, doi = {10.1109/ICMLA58977.2023.00244}, interhash = {9818328a6809ca9723fcf46b834e1bf2}, intrahash = {e6ecfa5a1489519a2c4c3b9f08ceb2ee}, pages = {1611--1618}, publisher = {IEEE}, title = {DADO – Low-Cost Query Strategies for Deep Active Design Optimization}, year = 2023 } @inproceedings{nivarthi2023towards, abstract = {Anomaly detection plays a pivotal role in diverse realworld applications such as cybersecurity, fault detection, network monitoring, predictive maintenance, and highly automated driving. However, obtaining labeled anomalous data can be a formidable challenge, especially when anomalies exhibit temporal evolution. This paper introduces LATAM (Long short-term memory Autoencoder with Temporal Attention Mechanism) for few-shot anomaly detection, with the aim of enhancing detection performance in scenarios with limited labeled anomaly data. LATAM effectively captures temporal dependencies and emphasizes significant patterns in multivariate time series data. In our investigation, we comprehensively evaluate LATAM against other anomaly detection models, particularly assessing its capability in few-shot learning scenarios where we have minimal examples from the normal class and none from the anomalous class in the training data. Our experimental results, derived from real-world photovoltaic inverter data, highlight LATAM’s superiority, showcasing a substantial 27% mean F1 score improvement, even when trained on a mere two-week dataset. Furthermore, LATAM demonstrates remarkable results on the open-source SWaT dataset, achieving a 12% boost in accuracy with only two days of training data. Moreover, we introduce a simple yet effective dynamic thresholding mechanism, further enhancing the anomaly detection capabilities of LATAM. This underscores LATAM’s efficacy in addressing the challenges posed by limited labeled anomalies in practical scenarios and it proves valuable for downstream tasks involving temporal representation and time series prediction, extending its utility beyond anomaly detection applications.}, author = {Nivarthi, Chandana Priya and Sick, Bernhard}, booktitle = {International Conference on Machine Learning and Applications (ICMLA)}, doi = {10.1109/ICMLA58977.2023.00218}, interhash = {2c7b944a23ce00dd5e4637ce2c572f31}, intrahash = {a4a29acb67656f837ca6e532fc88958d}, pages = {1444--1450}, publisher = {IEEE}, title = {Towards Few-Shot Time Series Anomaly Detection with Temporal Attention and Dynamic Thresholding}, year = 2023 }