Neural operators (e.g., Fourier Neural Operator, DeepONet) learn mappings between function spaces and increasingly replace expensive numerical simulations. In practice, however, the real process always deviates from the simulation, and real measurements are scarce, noisy, and often only partially available. Within this project, we investigate how an operator pretrained on simulation data can be corrected using few, partially covered observations of the real system (residual or delta modelling, sim-to-real transfer). This thesis lays the methodological foundation on public benchmark problems of operator learning.
First, a neural operator is to be trained on an established benchmark problem (e.g., Darcy flow). Subsequently, a synthetic sim-to-real gap is to be created, for instance through perturbed coefficients or additional terms in the reference model. The core of the thesis is the implementation and comparison of methods for residual correction of the pretrained operator from few observations, where observations are only partially covered due to masking. The relationship between observation coverage, number of measurements, noise level, and achievable correction quality is to be quantified systematically. As a baseline, at least Gaussian process regression is to be included. The resulting evaluation environment should be built for reuse.
Solid Python skills and experience with a deep learning framework (PyTorch preferred); fundamentals of machine learning; interest in numerical simulation and scientific computing. Prior knowledge of neural operators or Gaussian processes is helpful but not required.
Sebastian Baum
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In deep drawing of cylindrical parts, periodic shape deviations known as earing form at the rim. In a production line, the rim contour h(φ) can often only be captured on partial arcs, and depending on the measurement position, the angular orientation of the part relative to the tool is unknown. Within this project, this poses a well-defined estimation problem: how well can a periodic contour be reconstructed from partial, noisy observations, and under which conditions can the unknown rotation be estimated from the characteristic, unequal expression of the ears themselves?
Based on synthetic contour data (parametric test contours and contours from preliminary finite element runs), an estimation method for reconstructing h(φ) from partial arcs is to be implemented and its conditioning analysed as a function of coverage. Subsequently, registration of the unknown rotation via matching against the asymmetric earing signature is to be implemented (e.g., cross-correlation), and its success probability quantified systematically as a function of asymmetry level, noise level, and coverage. The results feed directly into the selection of the measurement position in the project. Optionally (research project), the rotation can alternatively be treated probabilistically as a latent variable.
Solid Python skills (NumPy/SciPy); fundamentals of signal processing or estimation theory (Fourier analysis, regression); a structured, mathematically rigorous way of working. Knowledge of forming technology is not required.
Sebastian Baum
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Modular process plants, such as the offshore PtX plant developed in the H2Mare project, are highly complex systems. Operating personnel require extensive process and plant knowledge in order to control the process safely and to respond appropriately in the event of a fault. However, the necessary information is distributed across a wide range of heterogeneous sources, including operating manuals, signal and alarm lists, and maintenance documentation, all of which have to be searched manually. Experience from offshore test campaigns shows that considerable potential for improvement in efficiency and reliability exists in dealing with alarm floods, system faults, and diagnostic processes.
A particular feature of modular plants according to VDI/VDE/NAMUR 2658 is that plant structure, service descriptions, state machines, messages, and OPC UA addressing are already available in machine readable form within the Module Type Package (MTP). This source has so far remained unused by document centred assistance approaches.
Generative Artificial Intelligence (GenAI) opens up new possibilities in this context. Operator assistance systems can combine live process data, alarm histories, formalised module descriptions, and experiential knowledge in order to support operating personnel in diagnosis, decision making, and maintenance.
The aim of this thesis is to design and prototypically implement a GenAI based operator assistance system that supports operating personnel in monitoring, diagnosis, maintenance, and fault handling. Drawing on live process data, alarm messages, MTP module descriptions, and plant documentation, the system is intended to provide context specific recommendations for action, fault analyses, and decision support.
The objective is a general, transferable architecture for GenAI assistance systems in modular process plants based on OPC UA and MTP. The concept is to build on LLM and RAG technologies, to access process and knowledge sources selectively through a tool interface, and to use the machine readable MTP descriptions as the primary source of plant structure and data point semantics. Since the target environment is a largely unmanned offshore platform with limited network connectivity, the system is to be operated entirely locally on edge hardware.
Development and validation are carried out on a modular PtX model plant with a SIMATIC PCS neo process control system at IAS.
Your tasks:
- Familiarisation with GenAI approaches (LLMs, RAG) and with modular automation according to MTP
- Design of a modular, extensible layered architecture
- Connection to the process control system and development of the knowledge base
- Prototypical implementation of a demonstrator including a user interface
- Evaluation with regard to practical suitability, robustness, and transferability
- Independent and methodical approach to research
- Basic knowledge of the structure and function of process control systems in plant automation
- Interest in generative AI and its application in industrial environments
- Initial experience with LLMs and RAG systems is an advantage
- Familiarity with standards such as OPC UA or MTP is an advantage
- Programming skills in Python are an advantage
Peter Pfeiffer
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The aim of this thesis is to develop a framework for consequence anticipation for autonomous mobile robots (AMRs) intended for use in autonomous patient transport [1]. In contrast to traditional obstacle avoidance, the system is designed to predict the potential consequences of actions in at least three dimensions: physical (e.g. the spread of liquids following a pipe burst), normative (e.g. violation of safety zones or etiquette) and social (e.g. disruption to pedestrian flows or footfall). By integrating these anticipatory capabilities, the robot is intended to make proactive decisions that go beyond simply avoiding immediate collisions and minimise long-term systemic risks.
The aim of this research project is to develop a conceptual approach that enables an AMR to model the consequences of its interaction with the environment. This research project will systematically investigate which classes of models are suitable for anticipating physical, normative and social consequences. In particular, the project involves familiarising oneself with possible architectures and models. The theoretical differentiation of the consequences will be carried out in consultation with the supervisor. For illustrative validation, scenarios will be developed in which anticipation offers a measurable advantage over reactive systems (e.g. detecting a leak and anticipating the extent of the water spillage versus simply stopping before reaching the water); implementation in NVIDIA Isaac Sim is optional. Finally, a concept for integrating prediction into the autonomous patient transport system is to be developed. Current developments (e.g. latent planning with JEPA models, NVIDIA Cosmos as world foundation models) must be taken into account.
- Python
- Previous experience of, or an interest in, working in a simulation environment (Isaac Sim)
Kathrin Fleissner
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