Key features

1. Efficiency

   • Fast design space exploration: compact scalable regression models for design automation, parametric studies, design space exploration, optimization, yield improvement, visualization, prototyping, and sensitivity analysis

   • Gain insight: knowledge discovery in sparse data sets, and knowledge extraction from large data sets

2. Accuracy

   • Best-in-class modeling techniques: highly accurate and efficient proprietary and state-of-the-art surrogate modeling algorithms

3. Ease-of-use

   • Expert know-how at your fingertips: sensible default settings, based on expert knowledge from various disciplines (e.g., machine learning, approximation theory, numerical analysis, statistics, optimization, ...), and also many expert options available

   • Powerful logging and profiling tools: intermediate models (and plots) stored for further reference, extensive logging of what is going on, profiling framework to track modeling progress

4. Automation

   • Active learning: automatic selection of data points (also known as adaptive sample selection, sequential design, or optimal experimental design)

   • Model selection: automatic selection of model type (e.g., ANN, SVM, rational model, ...) and model complexity (e.g., number of neurons and hidden layers, kernel function, order, ...)

5. Flexibility

   • Pluggable and extensible framework: easy integration of custom implementations (e.g., sampling strategies, model types, model selection criteria, hyperparameter optimization algorithms,...)

   • Flexible experimental environment: easy to setup and run different modeling experiments, easy to benchmark different techniques

   • Multi-platform: available for Windows, Mac OSX, and Unix/Linux platforms

6. Speed

   • Shorten time to market: lower cost and shorten process cycle time

   • Distributed computing: integration with cluster and grid middleware to transparently run simulations in parallel