Industrial simulator Simcenter PhysicsAI

CAD/CAM

Overview
Simcenter PhysicsAI trains data-driven surrogate models from historical CAE results to predict scalar KPIs and 3D fields directly on mesh or CAD inputs. Using geometric deep learning, the platform captures shape-to-performance relationships across physics domains and produces predictions orders of magnitude faster than traditional solver runs, enabling broader design exploration and reduced physical prototyping.

Key benefits

• Fast predictions (case-dependent speedup; reduces time required for design-space exploration)
• Solver-agnostic ingestion of native CAE results without reformatting or conversion
• No-code workflows that enable multi-disciplinary teams to run AI predictions on engineering models
• Direct operation on complex geometries and topologies via geometric deep learning for 3D field outputs
• Built-in similarity / out-of-distribution assessment to indicate when full-fidelity simulation validation is recommended
• Flexible deployment: local on-premises, secure HPC or cloud to keep IP under customer control

Featured capabilities

• Operate on native CAE meshes and CAD models to improve interoperability and reuse of legacy simulation assets
• Train models directly on mesh/CAD data without manual feature engineering
• Detect out-of-distribution geometries with similarity metrics to reduce prediction risk
• Integrate into existing CAE workflows (examples: Simcenter Hypermesh, Simcenter Simlab, Simcenter Inspire)
• Supports GPU-accelerated training (recommended) and CPU/HPC environments

Use cases & outcomes

• Evaluate large numbers of design concepts rapidly to increase iteration count versus solver-only loops
• Reduce prototyping and testing costs by using AI predictions early in the design process
• Support material and process optimization to reduce waste and improve sustainability
• Example: accelerated packaging design with near-instant predictions delivering cost savings and high agreement with full FEA in validated cases

How it works
Simcenter PhysicsAI trains surrogate models from historical simulation results using geometric deep learning to learn mappings from geometry to performance. The platform outputs field or scalar predictions for new geometries, provides quantitative validation metrics (e.g., MAE), and flags out-of-distribution cases via a similarity score for additional verification with full-fidelity simulation.

Technical specifications

• Product: Simcenter PhysicsAI (software)
• Approach: Geometric deep learning operating directly on meshes and CAD models
• Solver support: solver-agnostic ingestion of native CAE results
• Predictions: 3D field predictions and KPI outputs depending on training data
• Performance: enables predictions up to ~1000x faster than traditional solvers (case-dependent)
• Training data: effectiveness depends on problem complexity; practical evaluation recommended after ~10 representative result files; dozens–hundreds may be needed for robust models
• Deployment: local on-premises, HPC or cloud; GPU recommended for accelerated training (supports modern NVIDIA architectures)
• Integration: native add-ons/integrations with Simcenter Hypermesh, Simcenter Simlab, Simcenter Inspire and other CAE workflows
• Safety/trust: includes similarity (OOD) detection to indicate when full-fidelity validation is advised
• Typical domains: structural (FEA), CFD, electromagnetics, manufacturing/process simulations and other physics domains