Average Ratings 0 Ratings
Average Ratings 0 Ratings
Description
Measurements are always subject to deviations from their true values, a phenomenon known as measurement uncertainty, especially when assessing or calibrating measurement tools or methodologies. To ensure quality control, it is essential to accurately quantify this uncertainty. GUMsim®, which adheres to the latest Guide to the Expression of Uncertainty in Measurement (GUM) and its supplement 1, utilizes sophisticated computational algorithms that facilitate a more effective determination of measurement uncertainty in line with ISO/IEC 17025 standards. The process of determining measurement uncertainty involves a mathematical relationship and statistical analysis of all variables influencing the measurement outcomes. To streamline this process, GUMsim provides a user-friendly input environment designed for various measurement models. Additionally, it offers a range of pre-defined application models that function as templates tailored to assist you in conducting specific evaluations, making it easier to embark on your measurement uncertainty assessments. This feature not only enhances the user experience but also encourages consistency in measurement practices across different applications.
Description
STOCHOS is an advanced probabilistic AI solution designed specifically for engineering and research and development applications. It harnesses existing simulation, testing, and measurement data to swiftly predict new variants while providing uncertainty assessments for each prediction, allowing engineers to discern when to trust the results or opt for traditional solvers. Utilizing the DIM-GP framework, STOCHOS is effective even with limited datasets, ranging from just a few dozen to a few hundred samples, and can handle various data types such as scalars, signals, 2D and 3D fields, meshes, geometries, and images. Its capabilities include surrogate modeling, uncertainty quantification, Bayesian and multi-objective optimization, as well as multi-fidelity modeling and sensitivity analysis, along with generative geometry techniques. STOCHOS Flow, a user-friendly visual workbench, enables the creation of workflows without the need for coding, allowing teams to deploy them as web applications. The software operates on local hardware and can be installed offline, ensuring accessibility and privacy. Founded in 2018 in Grafing bei München, PI Probaligence is part of the CADFEM Group and has established itself as a technology partner with Ansys, promoting innovative solutions in engineering. Furthermore, its ability to integrate seamlessly into existing processes enhances productivity and drives efficiency in engineering teams.
API Access
Has API
No
API Access
Has API
No
Screenshots View All
No images available
Integrations
No details available.
Integrations
No details available.
Pricing Details
€870 one-time payment
Free Trial
No
Free Version
No
Pricing Details
Quote on request
Free Trial
No
Free Version
No
Deployment
Web-Based
No
On-Premises
No
iPhone App
No
iPad App
No
Android App
No
Windows
Yes
Mac
No
Linux
No
Chromebook
No
Deployment
Web-Based
No
On-Premises
Yes
iPhone App
No
iPad App
No
Android App
No
Windows
Yes
Mac
No
Linux
Yes
Chromebook
No
Customer Support
Business Hours
Yes
Live Rep (24/7)
No
Online Support
Yes
Customer Support
Business Hours
No
Live Rep (24/7)
No
Online Support
Yes
Types of Training
Training Docs
Yes
Webinars
No
Live Training (Online)
No
In Person
No
Types of Training
Training Docs
Yes
Webinars
Yes
Live Training (Online)
Yes
In Person
Yes
Vendor Details
Company Name
QuoData
Country
Germany
Website
www.quodata.de/gumsim
Vendor Details
Company Name
PI Probaligence GmbH
Founded
2018
Country
Germany
Website
probaligence.com
Product Features
Statistical Analysis
Analytics
No
Association Discovery
No
Compliance Tracking
No
File Management
No
File Storage
No
Forecasting
No
Multivariate Analysis
No
Regression Analysis
No
Statistical Process Control
No
Statistical Simulation
Yes
Survival Analysis
No
Time Series
No
Visualization
No
Product Features
Machine Learning
Deep Learning
No
ML Algorithm Library
No
Model Training
No
Natural Language Processing (NLP)
No
Predictive Modeling
No
Statistical / Mathematical Tools
No
Templates
No
Visualization
No