Masterthesis: ML-basierte Ersatzmodellbildung für thermohydraulische Netze
Core
Developing data-driven surrogate models to accelerate thermohydraulic network simulations and analyze sensitivity to input changes.
Role type
Master's thesis researcher (Machine Learning for Energy Systems)
Builds
Surrogate ML models for thermal network simulation
Domain
Energy / Thermohydraulics / Machine Learning
Deliverable
research
Required skills
Machine Learning, Python, thermohydraulic simulation tools, data analysis
Responsibilities
Develop and train ML models to predict network states and reaction dynamics; compare model approaches for accuracy and speed; investigate model transferability to other networks.