Optimization / Design Space Exploration Software pSeven using Machine Learning
Software platform for design space exploration and predictive modeling.
pSeven provides advanced design space exploration features for efficient parameter analysis (experimental design, optimization, and uncertainty quantification) as well as predictive modeling capabilities to build machine learning models from existing test and simulation data. Additionally, it includes a SmartSelection feature that automatically selects the most effective method for a given problem, allowing users to solve various engineering problems and discover optimal designs with just a few clicks, even without being a mathematics expert.
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basic information
◆ A Perfect Toolset for Design Space Exploration Utilizing a highly integrated toolset for design space exploration with a user-friendly GUI, we solve complex engineering problems. ◆ Equipped with Cutting-Edge Algorithms By automatically selecting advanced algorithms and methods for design space exploration, we can significantly reduce design lead times and further improve product characteristics. ◆ Featuring a Powerful Workflow Engine By formalizing the product development process and leveraging a powerful workflow engine, we enhance collaboration between departments.
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For detailed pricing information, please feel free to contact us.
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Applications/Examples of results
◆Achievements - Aerospace - Oil and Gas - Automotive - Biomedical - Electronic Engineering - Industrial Equipment - Turbo Machinery - Batteries and Fuel Cells, etc. For details on applications and achievements, please contact us.
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Company information
We provide SCSK's cutting-edge solutions best suited for manufacturing, including design and visualization, pre- and post-processing, CAE analysis, production technology analysis, material development solutions, MI, AI predictive modeling, optimization, PLM, and cost management.
![[Case Study] Book Off Corporation Co., Ltd.](https://image.mono.ipros.com/public/product/image/e53/2000311317/IPROS8007093136648106833.jpg?w=280&h=280)
