Research Interests

 

My research interests focus on the development and implementation of methods that allow the probabilistic investigation of complex system dynamics. Basically, I am working in the following four overlapping methodological domaines, often in the context of time-consuming determinisitic simulation codes and system observations or models consisting of a large amount of data.

 

 

Machine Learning Algorithms

 

Combining machine learning algorithms to perform

  • regression tasks to estimate meta models of complex system dynamics, e.g. via Gauss Processes
  • adaptive sampling approaches to investigate complex system behavior

 

 

Uncertainty & Sensitivity Analysis

 

Using advanced analysis approaches to investigate complex systems with focus on system safety and risk measures

  • via Monte Carlo Dynamic Event Trees (MCDET) to exploratively study and probabilistically evaluate the impact of stochastic state transitions on the system dynamics
  • via Uncertainty Analysis methods, i.e. estimation of Tolerance Limits via Wilks Approach, Bootstrapping or classical Monte Carlo Simulations 
  • via Sensitivity Analysis methods to identify major contributors to the response uncertainty of a system, i.e. by squared multiple correlation coefficient or Sobol Indices

 

 

Complex Network Analysis

 

Using network analysis and representation approaches to investigate

  • complex dependency patterns, e.g. multiple hazard impacts on systems
  • dynamic patterns of probablisitsc risk models, e.g. of critical event sequences

 

 

Bayesian Inference

 

Developping Bayesian approaches to detect and characterize multiple transition patterns in complex time series, such as paleo-climate observations