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What is STOCHOS?

STOCHOS is a cutting-edge probabilistic AI tool tailored for engineering and research and development sectors. It efficiently utilizes available simulation, testing, and measurement data to quickly forecast new variants while offering uncertainty assessments for each outcome, enabling engineers to determine when they can rely on the findings or revert to conventional solvers. Using the DIM-GP framework, STOCHOS proves effective even with smaller datasets, accommodating samples ranging from a handful to several hundred, and is capable of managing diverse data types, including scalars, signals, 2D and 3D fields, meshes, geometries, and images. Among its features are surrogate modeling, uncertainty quantification, Bayesian and multi-objective optimization, as well as multi-fidelity modeling and sensitivity analysis, in addition to generative geometry techniques. The user-friendly STOCHOS Flow visual workbench allows users to build workflows without requiring programming skills, enabling teams to implement these workflows as web applications easily. The software can be installed on local hardware and works offline, thus ensuring both accessibility and privacy for users. Established in 2018 in Grafing bei München, PI Probaligence operates as part of the CADFEM Group and has emerged as a key technology partner with Ansys, fostering pioneering solutions in the engineering domain. Moreover, its seamless integration into existing workflows not only boosts productivity but also enhances operational efficiency within engineering teams, making it a valuable asset in today's fast-paced technological landscape.

What is QMSys GUM?

The QMSys GUM Software is specifically developed to evaluate the uncertainty associated with physical measurements, chemical analyses, and calibration procedures. It offers three unique methods to calculate measurement uncertainty. The first method, GUF Method for linear models, is focused on linear and quasi-linear structures, adhering to the GUM Uncertainty Framework. This method calculates partial derivatives that represent the initial terms of a Taylor series, which helps in determining sensitivity coefficients for the equivalent linear model, and subsequently uses the Gaussian error propagation law to find the combined standard uncertainty. The second method, GUF Method for nonlinear models, is tailored for nonlinear scenarios where the outcomes show a symmetric distribution, using various numerical strategies such as nonlinear sensitivity analysis and higher-order sensitivity indices, along with quasi-Monte Carlo simulations that apply Sobol sequences. By incorporating these diverse methodologies, the software equips users with extensive tools for performing thorough uncertainty analysis in various measurement situations, ensuring robustness and precision in their results. Additionally, it enhances decision-making processes by providing clear insights into the levels of uncertainty involved.

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Integrations Supported

Additional information not provided

Integrations Supported

Additional information not provided

API Availability

API Availability

Pricing Information

Quote on request

Pricing Information

Pricing not provided
Free Trial Offered?

Supported Platforms

Windows
On-Prem
Linux

Supported Platforms

Windows

Customer Service / Support

Web-Based Support

Customer Service / Support

Standard Support
Web-Based Support

Training Options

Documentation Hub
Webinars
Online Training
On-Site Training

Training Options

Documentation Hub
On-Site Training

Company Facts

Organization Name

PI Probaligence GmbH

Date Founded

2018

Company Location

Germany

Company Website

probaligence.com

Company Facts

Organization Name

Qualisyst

Date Founded

1994

Company Location

Bulgaria

Company Website

www.qsyst.com/qualisyst_en.htm

Categories and Features

Machine Learning

Not specified

Categories and Features

Statistical Analysis

Analytics
Association Discovery
Compliance Tracking
File Storage
Multivariate Analysis
Regression Analysis
Statistical Simulation
Visualization

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