CAUSALITY

• Transition to graph based meta models enables considerations of interactions
• Using conditional probability models
• Perform conditional correlation analysis

Clustering

• find similar deformation patterns
• get quick overview of your data
• multiple methods available like hierarchical clustering, distance based clustering (e.g. k-means), density based spatial clustering or spectral clustering
  
  

statistics

• multiple descriptive statistical methods available
• evaluation over time, spatial or across simulations
• arbitrary entities from nodes to components or selections

projection

• Project high dimensional deformation pattern on a 2D map
• Fast assessment of deviations or bifurcations also over time
• Use multiple methods (SOFM, t-sne, pca, ica, umap, ...)
• Map deviations back to the geometry

Sensitivity

using dimension reduction enables sevaral methods for sensitivity analysis

• ANOVA
• Sobol Indices
• Surrogate Modelling
• ...

subtraction

• get a quick overview from your model deviations
• subtract arbitrary results on arbitrary entities (nodes, elements, pids, clusters, ...)
• perform model mapping to compare different models

causal chains

• Use graph based models to perform causal analysis
• Assessment of causalities by evaluating error propagation chains
• Event detection to simplify assessment across time steps