Engineering data science and AI

By replacing time-intensive solver runs with AI-powered surrogate models and reduced-order modeling, Simcenter’s AI capabilities help reduce the simulation queues that slow iterative design. Engineers spend less time waiting for results and more time making informed design decisions - accelerating the entire product development cycle from concept through validation.

Traditional design workflows are constrained by slow, iterative CAD and simulation cycles. Simcenter’s AI capabilities - spanning generative AI-based concept generation, geometric deep learning and multi-disciplinary design optimization - deliver results in seconds, giving engineers the freedom to evaluate significantly more design directions within the same project timeline.

Simcenter is purpose-built to democratize AI and machine learning across the entire engineering organization - not just data scientists or AI specialists. AI capabilities are embedded directly into familiar CAD and simulation environments through no-code, intuitive workflows designed for practicing engineers. Model training, data pipelines and algorithm tuning operate transparently in the background - so every team, regardless of background or organization size, can harness the competitive advantage of intelligent simulation without steep learning curves or dedicated AI resources.

Simcenter’s engineering data science and AI software delivers measurable value across the full spectrum of physics domains - structural integrity, crash performance, thermal management, computational fluid dynamics, manufacturing simulation and electromagnetic behavior - ensuring every engineering discipline benefits from intelligent acceleration, not just a select few. Organizations across aerospace, automotive, heavy equipment, industrial machinery, and energy sectors leverage this cross-domain capability to compress design cycles, reduce physical prototypes, lower development costs and bring higher-performing products to market faster.

AI models are trained on high-fidelity engineering data, with performance rigorously benchmarked using established error metrics - like mean absolute error (MAE) and MSE (Mean Squared Error). The results are explainable and validated and not black-box predictions. This provides engineers with full visibility to interrogate, interpret, and act on AI-generated insights with quantifiable confidence. Further validate AI-predicted results directly against simulation or test benchmarks, closing the loop between intelligent acceleration and engineering rigour.

AI capabilities in the Simcenter portfolio are designed to be flexible and accessible across a range of hardware environments. For teams prioritizing training speed and model performance, graphical processing unit (GPU) acceleration is strongly recommended, with support for modern architectures including NVIDIA's latest Blackwell microarchitecture, enabling significantly faster model training and inference at scale. Organizations running large, complex engineering programs are fully supported in high-performance computing (HPC) environments, providing the computational headroom that demanding simulation workloads require. For teams without dedicated GPU and HPC infrastructure, CPU-based training remains a supported option - ensuring hardware availability is never a barrier to adopting AI-accelerated simulation.

The executable digital twin (xDT) takes the intelligence built inside the Simcenter simulation environment and makes it instantly deployable across the broader enterprise. High-fidelity simulation is encapsulated into lightweight reduced-order models, which can be further embedded in executable digital twins, enabling engineers and operational stakeholders to interact with accurate digital representations of their products in real time. The result is faster, more informed decision-making at every stage of the product lifecycle - from early concept validation through to in-service performance monitoring - breaking down the traditional barriers between subject matter experts and the wider teams who depend on their insights.