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  pSeven Desktop  v2025.03
Desktop application for design exploration, predictive modeling, CAD/CAE integration and automation.

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  pSeven Enterprise  v2025.03
Low-code cloud-native platform to automate engineering processes at scale.

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  Design Exploration
Solve complex engineering problems with state-of-the-art tools for DoE, UQ and design optimization.

  Predictive Modeling
Predict response values for new designs, accelerate complex simulations and capture knowledge from vast amounts of data.

  Process Integration
Integrate all your CAD/CAE packages into a single workflow, explore virtual prototype behavior and eliminate repetitive tasks with automation.

  SmartSelection
Automatic choosing of appropriate and the most effective DoE, UQ, optimization and predictive modeling algorithms.

  Collaborative engineering
Improve collaboration within and between the teams with simultaneous multi-user access and co-authoring of the workflows in real-time.

  Server-side execution
Run many resource-consuming simulation workflows simultaneously without interruption with a built-in resource manager.

  Workflows as web apps
Share simulation methodologies with other users across and outside the organization in a from of workflow-powered web apps.

  Integration with PLM / SPDM
Boost PLM / SPDM systems with enhanced simulation workflow execution.

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pSevenCompanyPublicationsP7UC2024 - Creation of a Machine Learning-based predictive model using structural strength simulation data for the stringer heel connection in ships

P7UC2024 - Creation of a Machine Learning-based predictive model using structural strength simulation data for the stringer heel connection in ships

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Authors:

Shinwoo Jang, Application Engineer, RaonX

Journal / Event:

pSeven User Conference 2024

Abstract:

Keywords: P7UC2024, pSeven Desktop, Machine Learning, Shipbuilding

Contacts

France, 31100 Toulouse, Av. du Général de Croutte 42

+33 (0) 5 82-95-59-68

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P7UC2024 - Creation of a Machine Learning-based predictive model using structural strength simulation data for the stringer heel connection in ships

Authors:

Journal / Event:

Abstract:

Contacts

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