@book{tomforde2024organic,
  doi = {10.17170/kobra-202402269661},
  editor = {Tomforde, Sven and Krupitzer, Christian},
  interhash = {d52aab1767cbc221b939559efda6a11e},
  intrahash = {86afff6d4d016768281b7bbe6e75b126},
  publisher = {kassel university press},
  series = {Intelligent Embedded Systems},
  title = {Organic Computing -- Doctoral Dissertation Colloquium 2023},
  volume = 26,
  year = 2024
}

@book{tomforde2023organic,
  doi = {10.17170/kobra-202302107484},
  editor = {Tomforde, Sven and Krupitzer, Christian},
  interhash = {f693547c6fc1e572709e62fdcbca86d4},
  intrahash = {318e085e7a12dc6bc89c952b8fa7a79d},
  publisher = {kassel university press},
  series = {Intelligent Embedded Systems},
  title = {Organic Computing -- Doctoral Dissertation Colloquium 2022},
  volume = 24,
  year = 2023
}

@inproceedings{decke2024efficient,
  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.},
  author = {Decke, Jens and Jenß, Arne and Sick, Bernhard and Gruhl, Christian},
  booktitle = {International Conference on Architecture of Computing Systems (ARCS)},
  interhash = {ceb6a3a602af9ddff882d088aad22e17},
  intrahash = {e069d535438db13d7d37fd4dc45e9425},
  note = {(accepted)},
  publisher = {Springer},
  title = {An Efficient Multi Quantile Regression Network with Ad Hoc Prevention of Quantile Crossing},
  year = 2024
}

@inproceedings{decke2024structured,
  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.},
  author = {Decke, Jens and Wünsch, Olaf and Sick, Bernhard and Gruhl, Christian},
  booktitle = {International Conference on Architecture of Computing Systems (ARCS)},
  interhash = {ed6bd9576bb4b4cfbbce236485b92f99},
  intrahash = {bf3baf1e39a1493c9cf4fcb18c6a9334},
  note = {(accepted)},
  publisher = {Springer},
  title = {From Structured to Unstructured: A Comparative Analysis of Computer Vision and Graph Models in solving Mesh-based PDEs},
  year = 2024
}

@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
}