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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 Ansys Sherlock?

Ansys Sherlock distinguishes itself as the only electronics design platform that utilizes reliability physics, providing rapid and accurate predictions of the lifespan of electronic components, boards, and systems in the early design stages. This automated analysis tool streamlines the design workflow and effectively bypasses the conventional "test-fail-fix-repeat" cycle by enabling designers to thoroughly simulate the interactions among silicon, metal layers, semiconductor packages, printed circuit boards (PCBs), and assemblies, thereby pinpointing potential failure vulnerabilities caused by thermal, mechanical, and manufacturing stresses before prototype development. With a comprehensive library exceeding 500,000 components, Sherlock adeptly converts electronic computer-aided design (ECAD) files into intricate computational fluid dynamics (CFD) and finite element analysis (FEA) models. Each model generated is designed with accurate geometries and material properties, providing a detailed and thorough representation of stress data. This groundbreaking methodology not only improves the design process but also significantly shortens the time it takes for electronic products to reach the market, ultimately giving companies a competitive edge. Furthermore, the ability to preemptively identify and address issues during the design phase enhances the overall reliability and performance of the final products.

Media

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Media

Integrations Supported

Additional information not provided

Integrations Supported

Additional information not provided

API Availability

API Availability

Has API

Pricing Information

Quote on request

Pricing Information

Pricing not provided
Free Trial Offered?

Supported Platforms

Windows
On-Prem
Linux

Supported Platforms

SaaS

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
Webinars
Online Training

Company Facts

Organization Name

PI Probaligence GmbH

Date Founded

2018

Company Location

Germany

Company Website

probaligence.com

Company Facts

Organization Name

Ansys

Date Founded

1970

Company Location

United States

Company Website

www.ansys.com/products/structures/ansys-sherlock

Categories and Features

Machine Learning

Not specified

Categories and Features

CFD

Not specified

Thermal Analysis

Not specified

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