Accelerate product innovation by embedding AI and data science into your engineering workflows. From AI-powered surrogate models and reduced-order modeling to intelligent design exploration, quickly transform simulation and test data into actionable insight at every stage of the product design.
Discover how engineers, from designers to analysts, can:
Traditional solvers across structural, thermal, fluid, manufacturing and electromagnetic domains are computationally intensive - slowing down innovation. Geometric deep learning models trained on simulation data operate directly on meshes and CAD models, delivering full 3D field results in seconds. Engineers can evaluate designs in real time and run optimization studies without requiring specialist AI expertise.
Traditional design exploration is time-intensive and risks missing optimal outcomes. The SHERPA algorithm - hybrid, adaptive and AI-enhanced, learns from simulation and test data to navigate complex design spaces. Supporting Multidisciplinary Design Optimization (MDO), it synchronizes across engineering domains to identify high-performing designs, uncover better trade-offs and drive confident decisions faster.
High-fidelity FEA, CFD and DEM models demand significant computational resources and time. Reduced-order modeling simplifies the complexity of these models into ultra-fast, accurate representations using Proper Orthogonal Decomposition (POD), Krylov and neural network methods. Export via FMI, ONNX or C-code enables seamless integration into system simulation and digital twins, without sacrificing accuracy.
Static models can't keep pace with real-world complexity. Executable Digital Twins package physics-based, AI and reduced-order models into portable, self-contained instances - deployable at the edge, cloud or on-site. By continuously mirroring physical assets, xDTs enable real-time performance monitoring and predictive maintenance, empowering engineers to make data-driven decisions across the product lifecycle.
