AI and ML with Simcenter

Product designs now span multiple disciplines and generate countless variants to simulate. Manual evaluation is too slow.

Running high-fidelity simulations across structural, NVH, impact, fluid and thermal domains consumes significant time and resources, multiplied across every design iteration.

High-performance computing resources and running repeated physical prototype iterations consume a disproportionate share of engineering budgets, resulting in costs that compound with every design cycle. 

Vast amounts of simulation and test data remain locked in departmental silos, lacking the curation, traceability and semantic context required for enterprise-wide reuse and AI readiness.

Translating AI models and predictive insights from research into production engineering workflows is slowed by tool friction, integration gaps and low adoption - leaving value on the table long after models are built.

Routine data management, mesh setup and report generation consume the majority of engineering hours while poor cross-functional visibility creates friction between simulation, test and product teams when it matters most.