Malte Prieß | Publications

2026

CLOUD_LLM-IR
submitted
Jan 26
A Cloud-Native Architecture for Human-in-Control LLM-Assisted OpenSearch in Investigative Settings (submitted)

Puhani, B., Brehmer, K., and Prieß, M.

2026

Abs Bib

Complex criminal investigations are often hindered by the volume of unstructured evidence and the semantic gap between natural language investigative intent and technical search logic. To address this challenge, we present a design and feasibility study of a cloud-native microservices architecture tailored to private cloud deployments. The proposed system integrates Large Language Models into a “Human-in-Control” workflow that translates natural language queries into syntactically valid OpenSearch Domain-Specific Language expressions. We describe the implementation of a hybrid retrieval strategy within OpenSearch that combines BM25-based lexical search with nested semantic vector embeddings. The paper focuses on system design and preliminary functional validation, establishing an architectural baseline for empirical evaluation. Technical feasibility is demonstrated through a functional prototype, and a rigorous evaluation methodology is outlined using the Enron email dataset as a structural proxy for restricted investigative corpora.
@conference{Puhani2026, author = {Puhani, B. and Brehmer, K. and Prieß, M.}, booktitle = {Cloud Computing 2026, The Seventeenth International Conference on Cloud Computing, GRIDs, and Virtualization}, title = {{A} {C}loud-{N}ative {A}rchitecture for {H}uman-in-{C}ontrol {LLM}-{A}ssisted {O}pen{S}earch in {I}nvestigative {S}ettings (submitted)}, year = {2026}, publisher = {IARIA}, keywords = {Information Retrieval, Semantic Search, Investigative Settings, Enron Dataset} }
CLOUD_Mul-Cl
submitted
Jan 26
Towards Global Multi-Cloud Strategies: Insights into AWS and Alibaba Cloud Synergy (submitted)

Zizler, M. G., Neumann, C. P., and Prieß, M.

2026

Abs Bib

Multi-cloud strategies are increasingly adopted by modern enterprises to improve agility and resilience and to reduce vendor lock-in. Integrating workloads across providers such as AWS and Alibaba Cloud remains challenging due to interoperability and migration issues. This paper presents a comparative analysis of AWS and Alibaba Cloud focusing on architectural, ability and migration issues. This paper presents a comparative analysis of AWS and Alibaba Cloud focusing on architectural, service, and policy differences affecting workload migration. Using both provider-native and open source Infrastructure-as-Code tools, we conduct an exploratory case study about migration of IoT workloads. The results highlight key technical trade-offs and best practices for secure multi-cloud deployments, offering guidance for organizations pursuing AWS and Alibaba Cloud interoperability.
@conference{Zizler2026, author = {Zizler, M.~G. and Neumann, C.~P. and Prieß, M.}, booktitle = {Cloud Computing 2026, The Seventeenth International Conference on Cloud Computing, GRIDs, and Virtualization}, title = {{T}owards {G}lobal {M}ulti-{C}loud {S}trategies: {I}nsights into {AWS} and {A}libaba {C}loud {S}ynergy (submitted)}, year = {2026}, publisher = {IARIA}, keywords = {Cloud Computing, Multi-Cloud, AWS, Alibaba Cloud, Infrastructure-as-Code}, }

2025

AI_Vuln-Ass
Graph of Effort: Quantifying Risk of AI Usage for Vulnerability Assessment

Mehra, A., Aßmuth, A., and Prieß, M.

2025

Abs Bib Link

With AI-based software becoming widely available, the risk of exploiting its capabilities, such as high automation and complex pattern recognition, could significantly increase. An AI used offensively to attack non-AI assets is referred to as offensive AI. Current research explores how offensive AI can be utilized and how its usage can be classified. Additionally, methods for threat modeling are being developed for AI-based assets within organizations. However, there are gaps that need to be addressed. Firstly, there is a need to quantify the factors contributing to the AI threat. Secondly, there is a requirement to create threat models that analyze the risk of being attacked by AI for vulnerability assessment across all assets of an organization. This is particularly crucial and challenging in cloud environments, where sophisticated infrastructure and access control landscapes are prevalent. The ability to quantify and further analyze the threat posed by offensive AI enables analysts to rank vulnerabilities and prioritize the implementation of proactive countermeasures. To address these gaps, this paper introduces the Graph of Effort, an intuitive, flexible, and effective threat modeling method for analyzing the effort required to use offensive AI for vulnerability exploitation by an adversary. While the threat model is functional and provides valuable support, its design choices need further empirical validation in future work.
@conference{Mehra2025a, author = {Mehra, A. and Aßmuth, A. and Prieß, M.}, booktitle = {Cloud Computing 2025, The Sixteenth International Conference on Cloud Computing, GRIDs, and Virtualization 2025}, title = {{G}raph of {E}ffort: {Q}uantifying {R}isk of {AI} {U}sage for {V}ulnerability {A}ssessment}, year = {2025}, publisher = {IARIA}, copyright = {arXiv.org perpetual, non-exclusive license}, doi = {10.48550/ARXIV.2503.16392}, keywords = {Cryptography and Security (cs.CR), Artificial Intelligence (cs.AI), Distributed, Parallel, and Cluster Computing (cs.DC), FOS: Computer and information sciences, FOS: Computer and information sciences}, }
AI_DL-Metrics
Improving Applicability of Deep Learning based Token Classification models during Training

Mehra, A., Prieß, M., and Himstedt, M.

2025

Abs Bib Link

This paper shows that further evaluation metrics during model training are needed to decide about its applicability in inference. As an example, a LayoutLM-based model is trained for token classification in documents. The documents are German receipts. We show that conventional classification metrics, represented by the F1-Score in our experiments, are insufficient for evaluating the applicability of machine learning models in practice. To address this problem, we introduce a novel metric, Document Integrity Precision (DIP), as a solution for visual document understanding and the token classification task. To the best of our knowledge, nothing comparable has been introduced in this context. DIP is a rigorous metric, describing how many documents of the test dataset require manual interventions. It enables AI researchers and software developers to conduct an in-depth investigation of the level of process automation in business software. In order to validate DIP, we conduct experiments with our created models to highlight and analyze the impact and relevance of DIP to evaluate if the model should be deployed or not in different training settings. Our results demonstrate that existing metrics barely change for isolated model impairments, whereas DIP indicates that the model requires substantial human interventions in deployment. The larger the set of entities being predicted, the less sensitive conventional metrics are, entailing poor automation quality. DIP, in contrast, remains a single value to be interpreted for entire entity sets. This highlights the importance of having metrics that focus on the business task for model training in production. Since DIP is created for the token classification task, more research is needed to find suitable metrics for other training tasks.
@misc{Mehra2025b, author = {Mehra, A. and Prieß, M. and Himstedt, M.}, title = {Improving Applicability of Deep Learning based Token Classification models during Training}, year = {2025}, copyright = {arXiv.org perpetual, non-exclusive license}, doi = {10.48550/ARXIV.2504.01028}, keywords = {Computer Vision and Pattern Recognition (cs.CV), Computation and Language (cs.CL), Information Retrieval (cs.IR), FOS: Computer and information sciences, FOS: Computer and information sciences}, publisher = {arXiv}, }

2024

AI_Ethics
Ethische Rahmenbedingungen beim Einsatz von KI im Projektgeschäft – Gefahren und Chancen der KI

Prechel, K., and Prieß, M.

2024

Abs Bib Link

In diesem Kapitel wird der ethische Rahmen beim Einsatz von Künstlicher Intelligenz (KI) im Projektgeschäft beleuchtet. Mit dem Fortschritt der KI ergeben sich sowohl Chancen als auch Gefahren im Projektmanagement. Während KI die Effizienz und Präzision von Projekten steigern kann, birgt sie auch ethische Herausforderungen, insbesondere in Bezug auf Fairness, Datenschutz und Transparenz. Kerstin Prechel und Malte Prieß diskutieren die Relevanz ethischer Überlegungen im Projektmanagement und stellen Fragen zur Integration von KI in ethisch verantwortungsvolle Weise. Das Kapitel betont die Bedeutung von Transparenz, Verantwortung und Verifizierung im Umgang mit KI und bietet einen Einblick in die ethischen Aspekte des Einsatzes von KI im Projektgeschäft.
@inbook{Prechel2024, author = {Prechel, K. and Prie{\ss}, M.}, editor = {Bernert, C. and Scheurer, S. and Wehnes, H.}, pages = {47--55}, publisher = {UVK Verlag}, title = {{E}thische {R}ahmenbedingungen beim {E}insatz von {KI} im {P}rojektgeschäft -- {G}efahren und {C}hancen der {KI}}, year = {2024}, keywords = {Künstliche Intelligenz, Projektmanagement, Ethik, Datenschutz, Transparenz, Verantwortung, Verifizierung, Bias, Trainingsdaten, Entscheidungsfindung, Fairness}, isbn = {978-3-381-11132-9}, series = {KI in der Projektwirtschaft, Gesellschaft für Projektmanagement (GPM)}, }

2023

AI_Edu

KI lehren, KI lernen an der DHSH – Potentiale für die Bildungslandschaft

Prieß, M., and Prechel, K.

2023

Bib Link

@incollection{Priess2023,
  author = {Prie{\ss}, M. and Prechel, K.},
  booktitle = {INFORMATIK 2023 -- Designing Futures: Zukünfte gestalten},
  publisher = {Gesellschaft für Informatik e.V.},
  title = {{K}I lehren, {K}I lernen an der {DHSH} -- {P}otentiale für die {B}ildungslandschaft},
  year = {2023},
  address = {Bonn},
  isbn = {978-3-88579-731-9},
  pages = {457--460},
  doi = {10.18420/inf2023_56},
  pissn = {1617-5468},
}

2022

KI_Campus
Fellowship proposal "‘KI lehren, KI lernen an der DHSH"’, KI-Campus (Stifterverband für die Deutsche Wissenschaft e.V.) / accepted

Prieß, M.

2022

Abs Bib

Mit dem Fellow-Projekt möchte ich Lernressourcen und didaktische Konzepte zum Themenkomplex KI an der Duale Hochschule Schleswig-Holstein (DHSH) ausbauen und zur Digitalisierung an der DHSH beitragen, indem Lehrende den Umgang mit digitalen Lehr-Formaten und Studierende unterstützt durch KI-Campus Ressourcen KI-Einsatzkontexte erlernen. Hierzu beabsichtige ich, im Rahmen eines Moduls zum Thema Data Science und Maschinelles Lernen Ressourcen des KI-Campus im Sinne eines Blended-Learnings zu nutzen. Grundlegende Verfahren des Maschinellen Lernens, eine Einführung in künstliche neuronale Netze sowie einen Ausblick auf den Bereich Deep Learning sollen abgedeckt werden. Weiterhin möchte ich für eine gesellschaftliche Folgenabwägung und kritische Reflektion der eingesetzten Technologien über entsprechende Kurse sensibilisieren.
@article{Priess2022, author = {Prie{\ss}, M.}, title = {Fellowship proposal "`KI lehren, KI lernen an der DHSH"', KI-Campus (Stifterverband für die Deutsche Wissenschaft e.V.) / accepted}, year = {2022}, cite-key = {prpr2022a}, note = {accepted}, }
AI_Doc-Search
Evaluierung des Dense Passage Retrievals zur Dokumentensuche in Behörden im Vergleich zu BM25

Mehra, A., Prieß, M., and Peters, T. V.

2022

Abs Bib Link

Die Suche nach Dokumenten ist eine der Aufgaben, die Maschinen am häufigsten ausführen. Täglich genutzte Suchmaschinen suchen nach Dokumenten, um das Informationsbedürfnis eines Anfragenden zu erfüllen. Aktuelle Forschungsergebnisse zeigen, dass der Bewertungsansatz des Dense Passage Retrieval (DPR) – welcher die zu suchenden Dokumente mittels tiefer neuronaler Netze in Dense-Vektoren umwandelt – in englischer ebenso wie in deutscher Sprache bessere Ergebnisse liefert als aktuell implementierte Algorithmen wie der BM25-Algorithmus. Die Dokumentensuche in deutschen Behörden stellt eine besondere Aufgabe dar, da in diesen komplexe behördenspezifische Fachausdrücke verwendet werden. Im Rahmen dieser Arbeit wurde für die Dokumentensuche zu Corona-Regelungen in der öffentlichen Verwaltung ein DPR mit einem öffentlichen deutschsprachigen Datensatz trainiert und mit dem BM25-Algorithmus von Elasticsearch anhand der Relevanz als Gütemaß verglichen. Die Ergebnisse zeigen, dass das DPR mehr Dokumente mit gesuchten Informationen findet, Synonyme in Suchanfragen berücksichtigen kann und damit das Potenzial zur Verbesserung der Dokumentensuche in Behördenanwendungen hat.
@inbook{Mehra2022a, author = {Mehra, A. and Prie{\ss}, M. and Peters, T. V.}, pages = {353--362}, title = {{E}valuierung des {D}ense {P}assage {R}etrievals zur {D}okumentensuche in {B}ehörden im {V}ergleich zu {BM}25}, year = {2022}, volume = {AKWI-Tagungsband zur 35. AKWI-Jahrestagung}, cite-key = {mehra2022}, doi = {https://doi.org/10.30844/AKWI_2022_26}, journal = {Monographien}, keywords = {Dense Passage Retrieval, BM25, Natürliche Sprachverarbeitung, Künstliche Intelligenz, Neuronale Netze, Information Retrieval, Dokumentensuche}, }

2021

AI_Bank

KI am Anwendungsbeispiel der Zahlungskorrektur der PPI AG

Prieß, M., Janz, K., and Kraas, H.

In Posterausstellung anlässlich der Herbstsitzungen des Wissenschaftsrates 2021

Bib

@inproceedings{Priess2021,
  author = {Prie{\ss}, M. and Janz, K. and Kraas, H.},
  booktitle = {{P}osterausstellung anlässlich der {H}erbstsitzungen des {W}issenschaftsrates},
  title = {{KI} am {A}nwendungsbeispiel der {Z}ahlungskorrektur der {PPI AG}},
  year = {2021},
}

2014

SBO_OneShot
Surrogate-Based and One-Shot Optimization Methods for PDE-Constrained Problems with an Application in Climate Models

Slawig, T., Prieß, M., and Kratzenstein, C.

In Solving Computationally Expensive Engineering Problems 2014

Abs Bib Link

We discuss PDE-constrained optimization problems with iterative state solvers. As typical and challenging example, we present an application in climate research, namely a parameter optimization problem for a marine ecosystem model. Therein, a periodic state is obtained via a slowly convergent fixed-point type iteration. We recall the algorithm that results from a direct or black-box optimization of such kind of problems, and discuss ways to obtain derivative information to use in gradient-based methods. Then we describe two optimization approaches, the One-shot and the Surrogate-based Optimization method. Both methods aim to reduce the high computational effort caused by the slow state iteration. The idea of the One-shot approach is to construct a combined iteration for state, adjoint and parameters, thus avoiding expensive forward and reverse computations of a standard adjoint method. In the Surrogate-based Optimization method, the original model is replaced by a surrogate which is here based on a truncated iteration with fewer steps. We compare both approaches, provide implementation details for the presented application, and give some numerical results.
@inproceedings{Slawig2014a, author = {Slawig, T. and Prie{\ss}, M. and Kratzenstein, C.}, booktitle = {Solving Computationally Expensive Engineering Problems}, title = {Surrogate-Based and One-Shot Optimization Methods for PDE-Constrained Problems with an Application in Climate Models}, year = {2014}, editor = {Koziel, S. and Leifsson, L. and Yang, X.S.}, pages = {1--24}, publisher = {Springer}, series = {Springer Proceedings in Mathematics \& Statistics}, volume = {97}, keywords = {optimization, climate model, marine ecosystem model, one-shot method, surrogate-based optimization}, }

2013

SBO
Marine Ecosystem Model Calibration with Real Data Using Enhanced Surrogate-Based Optimization

Prieß, M., Koziel, S., and Slawig, T.

Journal of Computational Science 2013

Abs Bib Link

We have already shown in a previous methodological work that the surrogate-based optimization (SBO) approach can be successful and computationally very efficient when reconstructing parameters in a typical nonlinear, time-dependent marine ecosystem model, where a one-dimensional application has been considered to test the method’s functionality in a first step. The application on real (measurement) data is covered in this paper. Essential here are a special model data treatment and further methodological enhancements which allow us to improve the robustness of the algorithm and the accuracy of the solution. By numerical experiments, we demonstrate that SBO is able to yield a solution close to the original model’s optimum while time savings are again up to 85% when compared to a conventional direct optimization of the original model.
@article{Priess2013, author = {Prie{\ss}, M. and Koziel, S. and Slawig, T.}, journal = {Journal of Computational Science}, title = {Marine Ecosystem Model Calibration with Real Data Using Enhanced Surrogate-Based Optimization}, year = {2013}, number = {5}, pages = {423–437}, volume = {4}, keywords = {marine ecosystem model, surrogate-based optimization, model calibration, parameter identification, response correction, low-fidelity model}, }
SBO
Accelerated Parameter Identification in a 3D Marine Ecosystem Model Using Surrogate-Based Optimization

Prieß, M., Piwonski, J., Koziel, S., Oschlies, A., and Slawig, T.

Ocean Modelling 2013

Abs Bib Link

We present the application of the Surrogate-based Optimization (SBO) method on a parameter identification problem for a 3-D biogeochemical model. SBO is a method for acceleration of optimization processes when the underlying model itself is of very high computational complexity. In these cases, coupled simulation runs require large amounts of computer time, where optimization runs may become unfeasible even with high-performance hardware. As a consequence, the key idea of SBO is to replace the original and computationally expensive (high-fidelity) model by a so-called surrogate, which is created from a less accurate but computationally cheaper (low-fidelity) model and a suitable correction approach to increase its accuracy. To date, the SBO approach has been widely and successfully used in engineering applications and also for parameter identification in a 1-D marine ecosystem model of NPZD type. In this paper, we apply the approach onto a two-component biogeochemical model. The model is spun-up into a steady seasonal cycle via the Transport Matrix Approach. The low-fidelity model we use consists of a reduced number of spin-up iterations (several decades instead of millennia used for the original model). A multiplicative correction operator is further exploited to extrapolate the rather inaccurate low-fidelity model onto the original one. This corrected model builds our surrogate. We validate this SBO method by twin-experiments that use synthetic observations generated by the original model. We motivate our choice of the low-fidelity model and the multiplicative correction and discuss the computational advantage of SBO in comparison to an expensive parameter optimization in the context of the high-fidelity model. The proposed SBO technique is shown to yield a solution close to the target at a significant gain of computational efficiency. Without further regularization techniques, the method is able to identify most model parameters. The method is simple to implement and presents a promising and pragmatic tool to calibrate biogeochemical models in a global three-dimensional setting.
@article{Priess2013a, author = {Prie{\ss}, M. and Piwonski, J. and Koziel, S. and Oschlies, A. and Slawig, T.}, journal = {Ocean Modelling}, title = {Accelerated Parameter Identification in a 3D Marine Ecosystem Model Using Surrogate-Based Optimization}, year = {2013}, pages = {22–36}, volume = {68}, keywords = {marine biogeochemical model, parameter identification, model calibration, surrogate-based optimization, rsponse correction, low-fidelity model}, }
SBO
Marine Ecosystem Model Calibration through Enhanced Surrogate-Based Optimization

Prieß, M., Koziel, S., and Slawig, T.

2013

Abs Bib Link

Mathematical optimization of models based on simulations usually requires a substantial number of computationally expensive model evaluations and it is therefore often impractical. An improved surrogate-based optimization methodology, which addresses these issues, is developed for the optimization of a representative of the class of one-dimensional marine ecosystem models. Our technique is based upon a multiplicative response correction technique to create a computationally cheap but yet reasonably accurate surrogate from a temporarily coarser discretized physics-based coarse model. The original version of this methodology was capable of yielding about 84% computational cost savings when compared to the fine ecosystem model optimization. Here, we demonstrate that by employing relatively simple modifications, the surrogate model accuracy and the efficiency of the optimization process can be further improved. More specifically, for the considered test case, the optimization cost is reduced three times, i.e., from about 15% to only 5% of the cost of the direct fine model optimization.
@inbook{Priess2013b, author = {Prie{\ss}, M. and Koziel, S. and Slawig, T.}, editor = {Pina, Nuno and Kacprzyk, Janusz and Filipe, Joaquim}, pages = {193--208}, publisher = {Springer Berlin Heidelberg}, title = {Marine Ecosystem Model Calibration through Enhanced Surrogate-Based Optimization}, year = {2013}, isbn = {978-3-642-34336-0}, volume = {197}, booktitle = {Simulation and Modeling Methodologies, Technologies and Applications: International Conference, SIMULTECH 2011 Noordwijkerhout, The Netherlands, July 29-31, 2011 Revised Selected Papers}, doi = {10.1007/978-3-642-34336-0_13}, keywords = {Marine Ecosystem Models, Surrogate-based Optimization, Parameter Optimization, Response Correction, Data Assimilation}, url = {https://doi.org/10.1007/978-3-642-34336-0_13}, }

2012

SBO
Parameter Identification in Climate Models Using Surrogate-Based Optimization

Prieß, M., Piwonski, J., Koziel, S., and Slawig, T.

Journal of Computational Methods in Science and Engineering 2012

Abs Bib Link

We present initial steps and first results of a surrogate-based optimization (SBO) approach for parameter optimization in climate models. In SBO, a computationally cheap, but yet reasonably accurate representation of the original high-fidelity (or fine) model, the so-called surrogate, replaces the fine model in the optimization process. We choose two representatives, namely two marine ecosystem models, to verify our approach. We present two ways to obtain a physics-based low-fidelity (or coarse) model, one based on a coarser time discretization, the other on an inaccurate fixed point iteration. Since in both cases, the low-fidelity model is less accurate, we use a multiplicative response correction technique, aligning the low- and the high-fidelity model output to obtain a reliable surrogate at the current iterate in the optimization process. We verify the approach by using model generated target data. We show that the proposed SBO method leads to a very satisfactory solution at the cost of a few evaluations of the high-fidelity model only.
@article{Piwonski2012, author = {Prie{\ss}, M. and Piwonski, J. and Koziel, S. and Slawig, T.}, journal = {Journal of Computational Methods in Science and Engineering}, title = {Parameter Identification in Climate Models Using Surrogate-Based Optimization}, year = {2012}, pages = {47--62}, volume = {12}, keywords = {climate models, marine ecosystem models, parameter identification, parameter optimization, surrogate-based optimization, low-fidelity models, response correction}, }
SBO
Surrogate-Based Optimization for Marine Ecosystem Models

Prieß, M.

2012

Abs Bib Link

Marine ecosystem models are of great importance for understanding the oceanic uptake of carbon dioxide and for projections of the marine ecosystem’s responses to climate change. The applicability of a marine ecosystem model for prognostic simulations crucially depends on its ability to resemble the actually observed physical and biogeochemical processes. An assessment of the quality of a given model is typically based on its calibration against observed quantities. This calibration or optimization process is intrinsically linked to an adjustment of typically poorly known model parameters. Straightforward calibration attempts by direct adjustment of the model parameters using conventional optimization algorithms are often tedious or even beyond the capabilities of modern computer power as they normally require a large number of simulations. This typically results in prohibitively high computational cost, particularly if already a single model evaluation involves time-consuming computer simulations. The optimization of coupled hydrodynamical marine ecosystem models simulating biogeochemical processes in the ocean is here a representative example. Computing times of hours up to several days already for a single model evaluation are not uncommon. A computationally efficient optimization of expensive simulation models can be realized using for example surrogate-based optimization. Therein, the optimization of the expensive, so-called high-fidelity (or fine) model is carried out by means of a surrogate – a fine model’s fast but yet reasonably accurate representation. This work comprises an investigation and application of surrogate-based optimization methodologies employing physics-based low-fidelity (or coarse) models. Seeking a computationally efficient calibration of marine ecosystem models serves as the fundamental aim. As a case study, two illustrative marine ecosystem models are considered. Here, coarse models obtained by a coarser temporal resolution and by a truncated model spin-up are investigated. The accuracy of these computationally cheaper coarse models is typically not sufficient to directly exploit them in the optimization loop in lieu of the fine model. I investigate suitable correction techniques to ensure that the corrected coarse model (the surrogate) provides a reliable prediction of the fine model optimum. Firstly, I focus on Aggressive Space Mapping as one of the original Space Mapping approaches. It will be shown that this optimization method allows to achieve a reasonable reduction in the optimization costs, provided that the considered coarse and fine model are sufficiently “similar”. A multiplicative response correction approach, subsequently investigated, turned out to be very suitable for the considered marine ecosystem models. A reliable surrogate can be obtained. Exploiting the latter in a surrogate-based optimization algorithm, a computationally cheap but yet accurate solution is achieved. The optimization costs can be significantly reduced compared to what is achieved by the Aggressive Space Mapping algorithm. The proposed methodologies, particularly the multiplicative response correction approach, serve as initial parts of a set of tools for a computationally efficient calibration of marine ecosystem models. The investigation of further enhancements of the presented algorithms as well as other promising approaches in the framework of surrogate-based optimization will be highly valuable.
@phdthesis{Priess2012, author = {Prie{\ss}, M.}, school = {Mathematisch-Naturwissenschaftliche Fakultät, Christian-Albrechts-Universität zu Kiel, Institut für Informatik}, title = {Surrogate-Based Optimization for Marine Ecosystem Models}, year = {2012}, url = {https://macau.uni-kiel.de/receive/diss_mods_00007782?lang=de}, }
ASM
Aggressive space mapping for optimisation of a marine ecosystem model

Prieß, M., and Slawig, T.

International Journal of Mathematical Modelling and Numerical Optimisation 2012

Abs Bib Link

We apply the aggressive space mapping (ASM) algorithm to parameter optimisation of a one-dimensional marine ecosystem model. Such models are important to calculate the global carbon cycle and specifically the oceanic CO2 uptake. The aim of the optimisation is to find model parameters that minimise the misfit between model output and observational data. ASM first solves for an optimum of a computationally cheaper low-fidelity model that in our case is based on a coarser time discretisation while still guaranteeing numerical stability. Secondly, an approximate optimum of the high-fidelity model is iteratively found by a parameter mapping applying a Quasi-Newton method and Broyden update. The applicability of the ASM technique to the problem is verified by using synthetic target data. Results are compared to those of the direct high-fidelity model optimisation. We show that a very reasonable solution can be obtained while yielding a significant reduction in the total optimisation cost.
@article{Priess2012a, author = {Prie{\ss}, M. and Slawig, T.}, journal = {International Journal of Mathematical Modelling and Numerical Optimisation}, title = {Aggressive space mapping for optimisation of a marine ecosystem model}, year = {2012}, note = {PMID: 44732}, number = {1-2}, pages = {98-116}, volume = {3}, doi = {10.1504/IJMMNO.2012.044732}, keywords = {marine ecosystem models, surrogate-based optimisation, low-fidelity model, aggressive space mapping, ASM, globalised Quasi-Newton, numerical optimisation, inverse problems}, url = {https://www.inderscienceonline.com/doi/abs/10.1504/IJMMNO.2012.044732}, }

2011

SBO

Improved Surrogate-Based Optimization of Climate Model Parameters Using Response Correction

Prieß, M., Koziel, S., and Slawig, T.

In Int. Conf. Simulation and Modeling Methodologies, Technologies and Appl. (SIMULTECH 2011) Jul 2011

Bib Link

@inproceedings{Priess2011,
  author = {Prie{\ss}, M. and Koziel, S. and Slawig, T.},
  booktitle = {Int. Conf. Simulation and Modeling Methodologies, Technologies and Appl. (SIMULTECH 2011)},
  title = {Improved Surrogate-Based Optimization of Climate Model Parameters Using Response Correction},
  year = {2011},
  address = {Noordwijkerhout, The Netherlands},
  month = jul,
  pages = {449-457},
}

SBO
Surrogate-based optimization of climate model parameters using response correction

Prieß, M., Koziel, S., and Slawig, T.

Journal of Computational Science Jul 2011

Abs Bib Link

We present a computationally efficient methodology for the optimization of climate model parameters applied to a (one-dimensional) representative of a class of marine ecosystem models. We use a response correction technique to create a surrogate from a temporarily coarser discretized physics-based low-fidelity model. We demonstrate that replacing the direct parameter optimization of the high-fidelity ecosystem model by iteratively updating and re-optimizing the surrogate leads to a very satisfactory solution while yielding significant cost saving - about 84% when compared to the direct high-fidelity model optimization.
@article{Priess2011a, author = {Prie{\ss}, M. and Koziel, S. and Slawig, T.}, journal = {Journal of Computational Science}, title = {Surrogate-based optimization of climate model parameters using response correction}, year = {2011}, issn = {1877-7503}, number = {4}, pages = {335-344}, volume = {2}, doi = {10.1016/j.jocs.2011.08.004}, keywords = {climate models, marine ecosystem models, surrogate-based optimization, parameter optimization, response correction}, url = {https://www.sciencedirect.com/science/article/pii/S187775031100072X}, }

2010

SBO
Surrogate-Based Optimization of Biogeochemical Transport Models

Prieß, M., and Slawig, T.

In Proceedings of the 8th International Conference of Numerical Analysis and Applied Mathematics (ICNAAM 2010) Sep 2010

Abs Bib Link

First approaches towards a surrogate-based optimization method for a one-dimensional marine biogeochemical model of NPZD type are presented. The model, developed by Oschlies and Garcon [1], simulates the distribution of nitrogen, phytoplankton, zooplankton and detritus in a water column and is driven by ocean circulation data. A key issue is to minimize the misfit between the model output and given observational data. Our aim is to reduce the overall optimization cost avoiding expensive function and derivative evaluations by using a surrogate model replacing the high-fidelity model in focus. This in particular becomes important for more complex three-dimensional models. We analyse a coarsening in the discretization of the model equations as one way to create such a surrogate. Here the numerical stability crucially depends upon the discrete stepsize in time and space and the biochemical terms. We show that for given model parameters the level of grid coarsening can be choosen accordingly yielding a stable and satisfactory surrogate. As one example of a surrogate-based optimization method we present results of the Aggressive Space Mapping technique (developed by John W. Bandler [2, 3]) applied to the optimization of this one-dimensional biogeochemical transport model.
@inproceedings{Priess2010, author = {Prie{\ss}, M. and Slawig, T.}, booktitle = {Proceedings of the 8th International Conference of Numerical Analysis and Applied Mathematics (ICNAAM 2010)}, title = {Surrogate-Based Optimization of Biogeochemical Transport Models}, year = {2010}, month = sep, pages = {612-616}, series = {AIP Conference Proceedings. Mathematical and Statistical Physics}, volume = {1281}, doi = {10.1063/1.3498551}, keywords = {integral equations, numerical analysis, partial differential equations, ordinary and partial differential equations, boundary value problems}, }

SBO

Surrogate-Based Optimization of Biogeochemical Models

Prieß, M.

In Symposium des Cluster of Excellence “The Future Ocean” Sep 2010

Bib

@inproceedings{Priess2010a,
  author = {Prie{\ss}, M.},
  booktitle = {{S}ymposium des {C}luster of {E}xcellence “{T}he {F}uture {O}cean”},
  title = {Surrogate-Based Optimization of Biogeochemical Models},
  year = {2010},
  organization = {Cluster of Excellence “The Future Ocean”},
}

2008

GravWave
Subtraction-Noise Projection in Gravitational-Wave Detector Networks

Harms, J., Mahrdt, C., Otto, C., Prieß, M., and Danzmann, K.

Physical Review D Sep 2008

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In this paper, we present a successful implementation of a subtraction-noise projection method into a simple, simulated data analysis pipeline of a gravitational-wave search. We investigate the problem to reveal a weak stochastic background signal which is covered by a strong foreground of compact-binary coalescences. The foreground, which is estimated by matched filters, has to be subtracted from the data. Even an optimal analysis of foreground signals will leave subtraction noise due to estimation errors of template parameters which may corrupt the measurement of the background signal. The subtraction noise can be removed by a noise projection. We apply our analysis pipeline to the proposed future-generation space-borne Big Bang Observer mission which seeks for a stochastic background of primordial gravitational waves in the frequency range ∼0.1 Hz-1 Hz covered by a foreground of black-hole and neutron-star binaries. Our analysis is based on a simulation code which provides a dynamical model of a time-delay interferometer network. It generates the data as time series and incorporates the analysis pipeline together with the noise projection. Our results confirm previous ad hoc predictions which say that the Big Bang Observer will be sensitive to backgrounds with fractional energy densities below Ω=1E-16.
@article{Harms2008, author = {Harms, J. and Mahrdt, C. and Otto, C. and Prie{\ss}, M. and Danzmann, K.}, journal = {Physical Review D}, title = {Subtraction-Noise Projection in Gravitational-Wave Detector Networks}, year = {2008}, number = {12}, volume = {77}, }