This NVIDIA blog post describes how agentic AI workflows can be used to prepare and validate digital twins for physical AI systems, with agents capable of inspecting 3D scenes and authoring simulation-relevant data.
Real-time SHM digital twin of a long-span suspension bridge via PINN, fusing strain measurements with physics constraints for damage detection and remaining-life prediction.
Proposes a neural operator-based surrogate model for CFD-level transient thermal-hydraulic simulation of helical coil steam generators in SMRs, enabling real-time digital twin applications.
Neural networks predict 0D reduced-order model parameters from geometry, cutting cardiovascular flow error by over half and running complex pulmonary cases in under 2 seconds on a laptop.
arXiv preprint (copyright held by authors)HemodynamicsReduced OrderDigital Twin
