Simulation data
Many development departments keep thousands of simulation runs unused in their archives. Machine learning turns them into the basis for faster decisions on new projects.
- Which parameters drive my result?
- Do I still need this simulation at all?
- How do I reuse old studies on new projects?
What your data already contains
Every parameter study holds knowledge about which quantities drive the result. We make that knowledge usable: export data from ANSYS, Abaqus, LS-DYNA or other solvers as a table, define inputs and targets, train a model.
The model then answers questions that would otherwise need a new run, and shows which parameters matter most.
Integration with your tools
If needed, we connect models to simulation data management (SPDM) and CAD so predictions appear where your engineers work.
Related projects
Predicting residual wall thickness in water-assisted injection molding
A neural network predicts the residual wall thickness of a hollow plastic part from its process parameters. Run in reverse, it returns the process setting needed for a target wall thickness. A project with Plastic Innovation.
→ View projectDesigning EV charging cables with phase change material
Fast charging drives high currents through cables and battery systems, and they heat up. An ML model built on FEM and prototype data sizes the conductor for a given maximum temperature, using phase change material as a passive heat buffer.
→ View projectDoes this match your question?
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