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Automate engineering processes at scale

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  pSeven Desktop  v2026.05
Desktop application for design exploration, predictive modeling, CAD/CAE integration and automation.

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  pSeven Enterprise  v2026.04
Web-native low-code platform for design exploration and automation of 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 desktop 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.

Web-native

  Low-code automation
Capture complex engineering processes and define execution logic in a web browser thanks to a powerful low-code workflow engine.

  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 DIY workflow-powered web apps.

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

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pSevenCompanyPublicationsRecent progress in engineering design with MDO/AI

Recent progress in engineering design with MDO/AI

Authors:

Joseph Morlier, Professor of Aerospace Engineering, ISAE-SUPAERO/ICA

Journal / Event:

pSeven User Conference 2022

Keywords: P7UC2022, Optimization, Aerospace, Artificial Intelligence

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17 Av. Didier Daurat, Parc des Algorithmes Immeuble Platon, 31700 Blagnac, France

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

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Recent progress in engineering design with MDO/AI

Authors:

Journal / Event:

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