RAVEN provides a set of basic and advanced capabilities that ranges from data generation,                                                 data processing and data visualization.

A full set of RAVEN computational capabilities are listed below:

Computing capabilities:
- Parallel computation capabilities (multi-thread and multi-core)
- Supported operating systems: MAC, Linux and Windows
- Workstation and high performance computing (HPC) systems

Multi-steps analyses: 
RAVEN analyses are performed through a series of simulation steps. Each simulation step allows the user to perform a set of basic actions: - Multi-Run - Training of a ROM - Post-Process - IOStep More complex analyses are performed by simply assembling and linking a series of steps listed above. Creation and use of reduced order models (scikit-learn, TensorFlow and CROW libraries): - SVM - Gaussian process models - Linear models - Multi-class classifiers - Decision trees - Naive Bayes - Neighbors classifiers and regressors - Multi-dimensional interpolators - High dimension model reduction (HDMR) - Morse-Smale complex
- Dynamic Mode Decomposition
- Neural Networks (Deep Learning)

 Forward propagation of uncertainties: - MonteCarlo sampling - Grid sampling - Stratified Sampling - Factorial design - Response surface design - Multi-variate analysis - Generic Polynomial Chaos expansion Advance sampling methods: - Sobol index sampling - Adaptive sampling - Sparse grid collocation sampling - Dynamic event trees (DETs) Model capabilities: - Generic interface with codes - Custom code interfaces - Custom ad-hoc external models Data Post-Processing capabilities: - Data clustering - Data regression - Data dimensionality Reduction - Custom generic post-processors - Time-dependent data analysis - Data plotting Model parameter optimization: - Simultaneous perturbation stochastic approximation method
- Finite Difference Gradient Based Optimization
- Conjiugate Gradient Based Optimization

 Data management: - Data importing and exporting - Databases creation