Nadine Berner
Nadine Berner 

Research Interests

Uncertainty Analysis

Probability

Machine Learning
Bayesian Inference

Complex Systems

Time Series Analysis

Networks

 

 

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

Bayesian Inference

 

Developping Bayesian inference approaches to

  • detect and characterize multiple transition patterns in complex time series, such as paleo-climate observations via Linear Mixed Models
  • localize critical input regions via Gauss Processes

Complex Network Analysis

 

Using network analysis and representation approaches to investigate

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