Research in Engineering Risk Analysis
We perform research in the broad area of optimized decision-making in engineering systems under uncertainty. In particular, we work on reliability analysis, predictive modeling and sensitivity analysis, risk assessment and management methods.
Key research areas of the group include:
- Reliability analysis methods
- Risk assessment
- Bayesian predictive modeling
- Decision analysis
- Predictive maintenance & value of information
- Natural hazards
- Reliability of structures
- Reliability of sensing systems
Ongoing research projects
- IntelliWind: Intelligent systems for autonomous wind power plant operations, 2024-2028, funded by the Horizon Europe MSCA program
- BIG-ROHU: Development of a Health and Usage Monitoring System using Big Data Analyses, 2024-2026, funded by the Federal Ministry for Econonmic Affairs and Climate Action
- Hierarchical Bayesian analysis for engineering models, 2024-2026, funded by the Alexander von Humboldt Foundation
- Probabilistic digital twins of ship structures, 2024-2026, funded by the Alexander von Humboldt Foundation
- Model Reduction for Structural Reliability Analysis, 2024-2027, funded by the TUM Insititute of Advanced Studies
- Machine learning for data-informed structural reliability analysis, 2023-2025, funded by the Alexander von Humboldt Foundation
- X-RISK-CC: How to adapt to changing weather eXtremes and associated compound RISKs in the context of Climate Change, 2023-2026, funded by the Interreg Alpine Space Program
- INFRA.RELEARN: Intelligent Infrastructure Maintenance with Deep Reinforcement Learning, 2022-2026, funded by the Georg Nemetschek Institute Artificial Intelligence for the Built World
Cost-benefit analysis for flood polders at the Danube river. 2020-2024, funded by LfU