HyperMesh introduces a complete workflow for real-time performance prediction and evaluation, automating repetitive tasks through machine learning. The modeling environment includes advanced capabilities for geometry creation and editing, automatic midsurface extraction, both surface and mid-meshing, mesh quality correction, and optimized assembly management. This approach enables engineers to operate within a streamlined and efficient design process, reducing setup time and increasing accuracy from the earliest stages.

AI-powered geometric recognition enables automatic selection and editing of similar elements across the model, eliminating the need for manual intervention on each individual component. Advanced clustering algorithms and automated feature extraction simplify the handling of complex assemblies and drastically reduce the workload associated with modeling repetitive parts. The result is faster model definition and overall improvement in model quality and consistency.

Thanks to physicsAI technology, HyperMesh overcomes the limitations of traditional simulation cycles. By leveraging historical FEM data and geometric deep learning techniques, the software delivers fully animated physical predictions up to 100 times faster than conventional solvers. This allows engineers to visualize the structural behavior of a model within seconds, enabling fast and informed design decisions.

The integration of AI into the engineering workflow enables prediction of outcomes during the conceptual phase, enhancing the effectiveness of design choices and shortening overall development time.