SANCARE

STOCHOS is probabilistic AI for engineering and R&D. It learns from the simulation, test, and measurement data a team already has and predicts new variants in seconds, with an uncertainty on every prediction, so engineers know when to trust a result and when to run the solver instead. Built on DIM-GP, it works with small datasets, typically tens to a few hundred samples, and with scalars, signals, 2D and 3D fields, meshes, geometries, and images. Methods: surrogate modeling, uncertainty quantification, Bayesian and multi-objective optimization, multi-fidelity modeling, sensitivity analysis, and generative geometry. STOCHOS Flow, the visual workbench, builds the workflows without code and ships them to the team as web apps. Runs on your own hardware, installs offline. From PI Probaligence, founded 2018 in Grafing bei München, part of the CADFEM Group and an Ansys Technology Partner.

Scikit-learn provides simple and efficient tools for predictive data analysis. Scikit-learn is a robust, open source machine learning library for the Python programming language, designed to provide simple and efficient tools for data analysis and modeling. Built on the foundations of popular scientific libraries like NumPy, SciPy, and Matplotlib, scikit-learn offers a wide range of supervised and unsupervised learning algorithms, making it an essential toolkit for data scientists, machine learning engineers, and researchers. The library is organized into a consistent and flexible framework, where various components can be combined and customized to suit specific needs. This modularity makes it easy for users to build complex pipelines, automate repetitive tasks, and integrate scikit-learn into larger machine-learning workflows. Additionally, the library’s emphasis on interoperability ensures that it works seamlessly with other Python libraries, facilitating smooth data processing.