Dr.-Ing. Siming Bayer

• Since 11/2019: Researcher at Pattern Recognition Lab, Department of Computer Science, Friedrich-Alexander University

• 01/2015 to 10/2019: Doctoral Researcher at Pattern Recognition Lab, Department of Computer Science, Friedrich-Alexander University in collaboration with Siemens Healthineers AG
• 09/2016 to 12/2016: Researcher at Molecular Imaging Lab, Department of Biomedical Engineering, Peking University
• 10/2009 t0 09/2015: Student at Friedrich-Alexander University (B.Sc and M.Sc Biomedical Engineering)

2025

• Erforschung und Entwicklung von hybriden Netzmodellen

  (Third Party Funds Group – Sub project)

  Overall project: Hybride Netzmodelle als Basis für den optimalen Betrieb von Fernwärmenetzen
  Project leader: Siming Bayer
  Term: September 1, 2025 - August 31, 2028
  Acronym: Hyb_Mod_Net
  Funding source: Bundesministerium für Wirtschaft und Energie (BMWE)
  →More information

2024

• Coordinated grid protection based on machine learning methods

  (Third Party Funds Single)

  Project leader: Andreas Maier, Johann Jäger, Siming Bayer
  Term: July 1, 2024 - June 30, 2027
  Acronym: Netzschutz-KI
  Funding source: DFG-Einzelförderung / Sachbeihilfe (EIN-SBH)
  →More information

• Teilvorhaben: KI gestütztes Engpassmanagement und Netzwiederaufbau

  (Third Party Funds Group – Sub project)

  Overall project: GridAssist - Assistenzsysteme für eine optimierte automatisierte Systemführung in Verteilnetzen
  Project leader: Siming Bayer, Gert Mehlmann, Johann Jäger, Matthias Luther
  Term: December 1, 2024 - November 30, 2027
  Acronym: GridAssist
  Funding source: Bundesministerium für Wirtschaft und Energie (BMWE)
  Abstract

  TP3: Automated Optimal System Management in MS/HS Networks & Network Restoration

  AP 3.1: Automated Bottleneck and Fault Management Based on Machine Learning Methods

  AP 3.2: Stable Grid and Supply Restoration

  TP5: Competence Team for Interfaces and Databases

  AP 5.1: Creation of a Knowledge Database for Heterogeneous Power Grid Data Sources

  Automated Grid Management and Restoration through AI and Data Integration

  The increasing complexity and volatility of modern power grids necessitate intelligent, automated systems for optimal operation and rapid restoration. In TP3, we develop AI-driven solutions for automated bottleneck and fault management (AP 3.1) as well as stable and systematic grid restoration (AP 3.2) in medium- and high-voltage (MS/HS) networks. These systems leverage machine learning techniques—ranging from supervised and unsupervised learning to reinforcement learning—with a hybrid training approach incorporating domain knowledge via Physics-Informed Neural Networks (PINNs) and Known-Operator Learning. This ensures reliable, explainable decision-making in real-time operations and minimizes data requirements in safety-critical environments.

  In bottleneck management, predictive and event-triggered AI models autonomously suggest topology adjustments and flexibility deployment, factoring in a wide range of grid constraints and operational targets. Fault management employs multi-source data analysis for real-time fault detection, location, isolation, and resupply, utilizing both existing sensors and proposed novel measurements. For restoration, a dedicated assistance system is developed using a two-stage training process: supervised learning of standardized grid restoration procedures followed by reinforcement learning in a high-fidelity simulation environment. A single-agent model competes across simulation instances to evolve optimized strategies for power system recovery.

  To support these applications, TP5 (AP 5.1) establishes a unified graph-based knowledge database for integrating heterogeneous data sources—from GIS, SCADA, and metering systems—into a cohesive representation. This structure enables complex system queries and machine-readable context for AI models, forming the backbone of advanced operational analytics.

  The entire system architecture and algorithms are tested in AP 6.3 through a hybrid field test utilizing real-time simulation with OPAL-RT and live grid snapshots from Lechwerke’s control center. Assistance systems interface with the simulation via standard protocols, ensuring vendor-independent implementation and practical transferability. Experienced grid operators validate the system in realistic operational conditions, ensuring alignment with the requirements of future grid operations and energy transition goals.

  →More information

2023

• Artificial Intelligent as a Market Participant – Implications for Antitrust Law

  (FAU Funds)

  Project leader: Siming Bayer
  Term: January 15, 2023 - January 14, 2024
  Abstract

  Introduction: Antitrust laws (also known ascompetition laws) are designed to encourage strong competition and are designedto protect consumers from predatory commercial practices. The primary goals ofantitrust law are to ensure the functioning of the markets and to ensure faircompetition. A prominent example of an antitrust violation is illegal pricefixing. By definition, it is an agreement between competitors that fixes pricesor other competitive conditions, and thus violates the principle of the pricingmechanism through free market forces. A typical feature of illegal price fixingis verifiable communication (written or verbal) between human marketparticipants. However, in the age of artificial intelligence and e-commerce,the definition and the detection of this illegal practice faces new challengesas collusive behaviors that violate antitrust laws, such as the pricingmechanism, can be partially or fully automated [1]. Furthermore, thecommunications between market participants can be both overt and covert. Finally,market participants can be artificial agents which might affected by perverseinstantiation [2]. In other words, new technological possibilities areavailable to disguise illegal pricing policies and business practices.

  Recent research, mainly from theeconomic and jurisprudence point of view, concludes the intensive applicationof AI algorithms in E-commerce will increase the extend of known forms ofanticompetitive behaviors [3][4]. However, the questions regarding whether andto which extent collusive behaviors will emerge by AI itself (which is anunknown form of anticompetitive behaviors) are rarely understood. Feasibilitystudies and comprehensive analysis comprising the implementation of AI methods andvalidation of the derived hypothesis has not been conducted so far. Therefore,the main goals of this project are to investigate the possibilities of collusivebehaviors stimulated and/or emerged by AI algorithms on digital marketplace andderive consequences on the antitrust law as well as competition policies. Tothe best of our knowledge, this is the first time that a research project inthe field of Antitrust and AI (AAI) is focusing on the mathematical andalgorithmic perspective of the question to which extend the utilization of AImethods is facilitating the collusive behaviors in the era of digital economy.

  Objectives: In order to validate the hypothesesthat AI algorithms is able to develop and communicate collusive behaviors ondigital marketplaces both in overt and covert fashion, comprehensive emulatorsof online marketplaces in different setups will be implemented.  Furthermore, different communication channels(both overt and covert) of digital marketplaces will be discovered and understood,as it is highly relevant to the detection of collusive practices. Finally, differentonline trading scenarios utilizing AI algorithms will be established and theimpact on antitrust law and competition polices will be derived. In total, the mainaspects in the intended DFG-application can be summarized as follows:

  1.      Asthe research topic belongs to a highly interdisciplinary field, a comprehensiveliterature review is necessary to define the problem space of the research andis of great importance to conduct the subsequent experiments successfully.Therefore, a comprehensive literature review on the aspects of antitrust law, gametheory, artificial intelligence and cyber security will be conducted.

  2.      Firststep of the implementation is the holistic emulation of the digitalmarketplace. The market emulator should have the capability to emulate the digitalmarket following various rules (e.g., Cournot vs. collusive competition) indifferent size (i.e., with different amount of market participants). Moreover,state-of-the-art algorithms for dynamic pricing should be replicated andintegrated into the market emulator as well.

  3.      Afurther aspect of this project is the communication mechanism in the era of E-commerceand AI. The know form of collusions mostly utilize overt communications.However, covert communication channels (i.e., communication channels that are notoriginally designed for the communication purpose, therefore hardly to bedetected [5][6]) poses further vulnerabilities of online marketplaces. The mechanismsand capacities of covert channels facilitating the collusive behaviors (e.g.,illegal price fixing) as should be investigated with the implemented marketemulator.

  4.      Finally,artificial agents for price definition of different products should be proposedand implemented following different competition models as well as marketcomplexities, aiming at understanding the central research questions of thisresearch project, i.e., capabilities and conditions of emerging collusivebehaviors of artificial agents by themselves. This particular step can beachieved by using reinforcement learning techniques. Technical opportunitiesand challenges for the discrimination of collusive and non-collusive behaviorsthat are potentially emerged by the artificial agents should be explored aswell.

  The entire project will besupervised by experts from three disciplines. Prof. Jochen Hoffmann (chair of Private Business Law) will support this research project with his knowledges and expertiseon antitrust law, Prof. Felix Feilling (chair of Cyber Security) will advise onthe aspects that are related to covert communication and cyber security, andProf. Andreas Maier (pattern recognition lab) will mentor this project from theAI point of view.

  [1] KünstlicheIntelligenz als Marktteilnehmer – Technische Möglichkeiten, Maier A., Bayer S.,Mohr Verlag. Submitted, unpublished.

  [2] BostromN. Superintelligence: Paths, Dangers, Strategies. Minds & Machines 25, Seite 285–289 (2015).

  [3] Petit, N. Antitrust andartificial intelligence: A research agenda. In: Journal of European CompetitionLaw and Practice. Vol. 8, Issue 6, pp. 361–362. Oxford University Press.(2017)

  [4] Beneke, F., Mackenrodt, M.,Remedies for algorithmic tacit collusion, Journal of Antitrust Enforcement,Volume 9, Issue 1, Pages 152–176 (2021).

  [5]Hans-Georg Eßer, Felix C. Freiling. Kapazitätsmessung eines verdecktenZeitkanals über HTTP, Univ. Mannheim,Technischer Bericht TR-2005-10, November 2005. (2005)

  [6] Freiling F.C., Schinzel S.Detecting Hidden Storage Side Channel Vulnerabilities in NetworkedApplications. In: Camenisch J., Fischer-Hübner S., Murayama Y., Portmann A.,Rieder C. (eds) Future Challenges in Security and Privacy for Academia andIndustry. SEC 2011. IFIP Advances in Information and Communication Technology,vol 354. Springer, Berlin, Heidelberg. (2011)

  →More information

• AI-refined thermo-hydraulic model for the improvement of the efficiency and quality of water supply

  (Third Party Funds Single)

  Project leader: Siming Bayer, Andreas Maier
  Term: November 1, 2023 - October 31, 2026
  Acronym: OptiHyd
  Funding source: Bayerisches Staatsministerium für Wirtschaft, Landesentwicklung und Energie (StMWi) (seit 2018)
  Abstract

  The United Nations' goals for sustainable development have made improving quality of life and access to clean drinking water a political priority. However, in recent decades, the water cycle in Bavaria has also been significantly affected by climate change. Two important aspects of daily drinking water supply and distribution are the assurance of water quality and the increase in usage efficiency. To enhance the resilience and capacity of the water supply in general, numerical simulation, data integration, and artificial intelligence (AI) are necessary. In this project, we aim to develop an AI-refined temperature-hydraulic model using heterogeneous data sources from a Bavarian water supply network. Hybrid AI methods are employed to model the complex relationship between water and soil temperature. The resulting model will serve as the basis for various real applications such as leak detection, anomaly recognition, and monitoring of drinking water quality, with the overarching goal of increasing the efficiency and quality of the water supply while simultaneously contributing to the containment of the impact of climate change on drinking water supply

  →More information

2021

• UtilityTwin

  (Third Party Funds Group – Overall project)

  Project leader: Andreas Maier, Siming Bayer, Adithya Ramachandran
  Term: September 1, 2021 - August 31, 2024
  Acronym: UtilityTwin
  Funding source: Bayerisches Staatsministerium für Wirtschaft, Landesentwicklung und Energie (StMWi) (seit 2018)
  Abstract

  In the UtilityTwin research project, an intelligent digital twin for any energy or water supply network is to be researched and developed on the basis of adaptive high-resolution sensor data (down to the sub-second range) and machine learning techniques. Overall, the technology concepts BigData and AI are to be combined in an innovative way in this research project in order to make positive contributions to the implementation of the energy transition and to counteract climate change.

  →More information

2016

• Intraoperative brain shift compensation and point-based vascular registration

  (Non-FAU Project)

  Project leader: Andreas Maier, Siming Bayer
  Term: May 1, 2016 - October 31, 2019
  →More information