About¶
pSeven Core is a powerful Python-based toolkit for predictive modeling, data analysis and optimization. It provides state-of-the-art algorithms for optimization, approximation, dimension reduction, design of experiments, and sensitivity analysis, including both well-known and unique modern methods.
pSeven Core allows you to:
- Reduce design time and cost thanks to exclusive design optimization technology based on the synergy between intelligent data analysis and numerical optimization.
- Improve product performance, quality, reliability and safety thanks to unmatched optimization and process automation capabilities.
- Perform fewer full-scale experiments and simulations with heavy codes using accurate and robust surrogate models.
- Reuse already available in-house data employing innovative and convenient methods to automatically train approximation models.
- Study your data with rich capabilities on sensitivity and correlations analysis.
Getting Started¶
If you do not have pSeven Core installed yet, see Setup and Support. Note that pSeven Core requires a license to run; see License Setup after Installation.
For a few simple tutorials, see Quick Start. More in-depth examples with detailed descriptions are found in Examples. Advanced pSeven Core usage is shown in the additional Code Samples.
Main part of this manual begins with a brief overview of the pSeven Core toolkit (Introduction), followed by detailed user’s guides for pSeven Core components and the API Reference.