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.

@article{botache2024enhancing, author = {Botache, Diego and Decke, Jens and Ripken, Winfried and Dornipati, Abhinay and Götz-Hahn, Franz and Ayeb, Mohamed and Sick, Bernhard}, title = {Enhancing Multi-Objective Optimization through Machine Learning-Supported Multiphysics Simulation}, journal = {arXiv e-prints}, year = {2024}, pages = {arXiv:2309.13179v2}, url = {https://arxiv.org/abs/2309.13179}, keywords = {imported, itegpub, isac-www, Electric_Motors, Multiobjective_Optimisation, Surrogate-Modelling, Deep-Learning, Explainable_Artificial_Intelligence}, 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.} }

%0 = article %A = Botache, Diego and Decke, Jens and Ripken, Winfried and Dornipati, Abhinay and Götz-Hahn, Franz and Ayeb, Mohamed and Sick, Bernhard %D = 2024 %T = Enhancing Multi-Objective Optimization through Machine Learning-Supported Multiphysics Simulation %U = https://arxiv.org/abs/2309.13179

This article presents the Sorting Composite Quantile Regression Neural Network (SCQRNN), an advanced quantile regression model designed to prevent quantile crossing and enhance computational efficiency. Integrating ad hoc sorting in training, the SCQRNN ensures non-intersecting quantiles, boosting model reliability and interpretability. We demonstrate that the SCQRNN not only prevents quantile crossing and reduces computational complexity but also achieves faster convergence than traditional models. This advancement meets the requirements of high-performance computing for sustainable, accurate computation. In organic computing, the SCQRNN enhances self-aware systems with predictive uncertainties, enriching applications across finance, meteorology, climate science, and engineering.

@inproceedings{decke2024efficient, author = {Decke, Jens and Jenß, Arne and Sick, Bernhard and Gruhl, Christian}, title = {An Efficient Multi Quantile Regression Network with Ad Hoc Prevention of Quantile Crossing}, booktitle = {International Conference on Architecture of Computing Systems (ARCS)}, publisher = {Springer}, year = {2024}, note = {(accepted)}, keywords = {imported, itegpub, isac-www, QuantileRegression, QuantileCrossing, OrganicComputing, Self-Awareness, DifferentiableSorting}, abstract = {This article presents the Sorting Composite Quantile Regression Neural Network (SCQRNN), an advanced quantile regression model designed to prevent quantile crossing and enhance computational efficiency. Integrating ad hoc sorting in training, the SCQRNN ensures non-intersecting quantiles, boosting model reliability and interpretability. We demonstrate that the SCQRNN not only prevents quantile crossing and reduces computational complexity but also achieves faster convergence than traditional models. This advancement meets the requirements of high-performance computing for sustainable, accurate computation. In organic computing, the SCQRNN enhances self-aware systems with predictive uncertainties, enriching applications across finance, meteorology, climate science, and engineering.} }

%0 = inproceedings %A = Decke, Jens and Jenß, Arne and Sick, Bernhard and Gruhl, Christian %B = International Conference on Architecture of Computing Systems (ARCS) %D = 2024 %I = Springer %T = An Efficient Multi Quantile Regression Network with Ad Hoc Prevention of Quantile Crossing

This article investigates the application of computer vision and graph-based models in solving mesh-based partial differential equations within high-performance computing environments. Focusing on structured, graded structured, and unstructured meshes, the study compares the performance and computational efficiency of three computer vision-based models against three graph-based models across three datasets. The research aims to identify the most suitable models for different mesh topographies, particularly highlighting the exploration of graded meshes, a less studied area. Results demonstrate that computer vision-based models, notably U-Net, outperform the graph models in prediction performance and efficiency in two (structured and graded) out of three mesh topographies. The study also reveals the unexpected effectiveness of computer vision-based models in handling unstructured meshes, suggesting a potential shift in methodological approaches for data-driven partial differential equation learning. The article underscores deep learning as a viable and potentially sustainable way to enhance traditional high-performance computing methods, advocating for informed model selection based on the topography of the mesh.

@inproceedings{decke2024structured, author = {Decke, Jens and Wünsch, Olaf and Sick, Bernhard and Gruhl, Christian}, title = {From Structured to Unstructured: A Comparative Analysis of Computer Vision and Graph Models in solving Mesh-based PDEs}, booktitle = {International Conference on Architecture of Computing Systems (ARCS)}, publisher = {Springer}, year = {2024}, note = {(accepted)}, keywords = {imported, itegpub, isac-www, Organic, Computing, Self-Optimization, DeepLearning, PartialDifferentialEquation, SurrogateModel, MeshTopographies}, abstract = {This article investigates the application of computer vision and graph-based models in solving mesh-based partial differential equations within high-performance computing environments. Focusing on structured, graded structured, and unstructured meshes, the study compares the performance and computational efficiency of three computer vision-based models against three graph-based models across three datasets. The research aims to identify the most suitable models for different mesh topographies, particularly highlighting the exploration of graded meshes, a less studied area. Results demonstrate that computer vision-based models, notably U-Net, outperform the graph models in prediction performance and efficiency in two (structured and graded) out of three mesh topographies. The study also reveals the unexpected effectiveness of computer vision-based models in handling unstructured meshes, suggesting a potential shift in methodological approaches for data-driven partial differential equation learning. The article underscores deep learning as a viable and potentially sustainable way to enhance traditional high-performance computing methods, advocating for informed model selection based on the topography of the mesh.} }

%0 = inproceedings %A = Decke, Jens and Wünsch, Olaf and Sick, Bernhard and Gruhl, Christian %B = International Conference on Architecture of Computing Systems (ARCS) %D = 2024 %I = Springer %T = From Structured to Unstructured: A Comparative Analysis of Computer Vision and Graph Models in solving Mesh-based PDEs

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.

@incollection{botache2023machine, author = {Botache, Diego}, title = {Machine Learning Supported Optimisation and Experimental Evaluation of Electrical Motors for Small Urban Passenger Vehicles}, editor = {Tomforde, Sven and Krupitzer, Christian}, booktitle = {Organic Computing -- Doctoral Dissertation Colloquium 2023}, publisher = {kassel university press}, year = {2023}, pages = {14--26}, keywords = {imported, itegpub, isac-www, electrical_traction_machines, sustainable_mobility, data-driven_models, machine_learning, multi-objective_optimisation, experimental_evaluation, deep_learning_techniques, test_benches}, 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. } }

%0 = incollection %A = Botache, Diego %B = Organic Computing -- Doctoral Dissertation Colloquium 2023 %D = 2023 %I = kassel university press %T = Machine Learning Supported Optimisation and Experimental Evaluation of Electrical Motors for Small Urban Passenger Vehicles

In the field of design optimization, numerical equation solvers,
mmonly used for finite element analysis and computational fluid dy-
mics, impose significant computational demands. The iterative nature
design optimization, involving numerous simulations, magnifies re-
urce requirements in terms of time, energy, costs, and greenhouse gas
issions. Accelerating this process has been a long-standing research
allenge, driven by the potential for resource savings and the ability
tackle increasingly complex problems. Recently, organic computing
thods have gained prominence as promising approaches to address
is challenge. This study aims to bridge the gap by integrating tech-
ques from deep learning, active learning, and generative learning into
e field of design optimization. The goal is to accelerate the design op-
mization process while addressing specific challenges such as exploring
rge and complex design spaces and evaluating the advantages and con-
raints of these methods. This research has the potential to significantly
pact the industrial use of design optimization by providing faster and
re efficient tools.

@incollection{decke2023examination, author = {Decke, Jens}, title = {An Examination of Organic Computing Strategies in Design Optimization}, editor = {Tomforde, Sven and Krupitzer, Christian}, booktitle = {Organic Computing -- Doctoral Dissertation Colloquium 2023}, publisher = {kassel university press}, year = {2023}, pages = {41--54}, keywords = {imported, itegpub, isac-www, DesignOptimization, DeepLearning, ActiveLearning, GenerativeLearning}, abstract = { In the field of design optimization, numerical equation solvers,
mmonly used for finite element analysis and computational fluid dy-
mics, impose significant computational demands. The iterative nature
design optimization, involving numerous simulations, magnifies re-
urce requirements in terms of time, energy, costs, and greenhouse gas
issions. Accelerating this process has been a long-standing research
allenge, driven by the potential for resource savings and the ability
tackle increasingly complex problems. Recently, organic computing
thods have gained prominence as promising approaches to address
is challenge. This study aims to bridge the gap by integrating tech-
ques from deep learning, active learning, and generative learning into
e field of design optimization. The goal is to accelerate the design op-
mization process while addressing specific challenges such as exploring
rge and complex design spaces and evaluating the advantages and con-
raints of these methods. This research has the potential to significantly
pact the industrial use of design optimization by providing faster and
re efficient tools.} }

%0 = incollection %A = Decke, Jens %B = Organic Computing -- Doctoral Dissertation Colloquium 2023 %D = 2023 %I = kassel university press %T = An Examination of Organic Computing Strategies in Design Optimization