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Automate engineering processes at scale

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  pSeven Desktop  v2025.06
Desktop application for design exploration, predictive modeling, CAD/CAE integration and automation.

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  pSeven Enterprise  v2025.06
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.

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

  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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Multidimensional optimization of loudspeakers

Alfred J. Svobodnik, CEO, Mvoid Audio Technologies
pSeven User Conference 2024

P7UC2024 Electronics Machine Learning Optimization

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

Shinwoo Jang, Application Engineer, RaonX
pSeven User Conference 2024

P7UC2024 pSeven Desktop Machine Learning Shipbuilding

RSM surrogate model under monotonic constraint for enhanced system monitoring and control

Quentin Courjeau, Air Systems Engineer, Liebherr Aerospace
DATADVANCE User Conference 2021

DUC2021 Surrogate Modeling Machine Learning Aerospace Artificial Intelligence

Prediction of ship roll motion using machine learning-based surrogate model

Young-Rong Kim, Jun-Bum Park, Serng—Bae Moon
J . Navig. Port Res. Vol. 42, No. 6 : 395-405, December 2018

Shipbuilding Machine Learning Surrogate Modeling

Data-driven model for hydraulic fracturing design optimization. Part II: Inverse problem

Viktor Duplyakov, Anton Morozov, Dmitriy Popkov, Egor Shel, Albert Vainshtein, Evgeny Burnaev, Andrei Osiptsov, Grigory Paderin
Cornell University

Optimization Oil and Gas Machine Learning Predictive Modeling Surrogate-Based Optimization Energy

Satellite battery degradation prognostic based on data analytics and big data infrastructure

Loïc BOUSSOUF, Clémentine BARREYRE, Anaïs CHARCOSSET, Airbus Defence & Space
10th International Symposium on NDT in Aerospace

Approximation Machine Learning Predictive Modeling Aerospace Battery Electronics

How machine learning empowers models for Digital Twins

Dmitry Frolov
BENCHMARK July 2018

Digital Twin Machine Learning Aerospace

Contacts

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

info@pseven.io

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