From concept assessment through design and simulation to validation. We take on clearly scoped tasks and combine classic engineering with AI wherever it improves the result.
Heads of R&D, project leads, technical procurement
Requirements analysis and concept assessment, CAD-related design, sizing for structure, thermal behaviour and function, CAE with FEM and CFD, parameter studies, test planning and reconciling simulation, AI prediction and reality.
What you get Solid results for your development decision
Together we pick a use case with real impact, take over a clearly scoped task and deliver a result you can verify.
What you get A result plus a blueprint for applying the approach to further projects
Phase 1 sets goals, KPIs and feasibility. Phase 2 builds prototype models on your data (e.g. regression, surrogate models, GNNs) and measures accuracy, iteration time and cost leverage. Phase 3 prepares integration and scale-up.
What you get Quantified value with clearly stated limits and a decision basis for rollout
We combine physical models and simulation with machine learning. The result runs faster than the simulation and knows more than a purely data-driven model.
What you get A model for fast prediction, inverse design or use in control and testing
Pick a use case with high simulation effort or a critical decision.
Solve a scoped task and measure the value against agreed KPIs.
Extend to further components, variants and programmes.
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 projectFast 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 projectDescribe the task. We will come back with a first view on approach, data and duration.