Newtwen's proprietary MOR engine compresses massive FEA models into a compact, real-time digital twin:

1. From millions of equations in the Full-Order Model to just a few tenths in the Reduced Model.

2. Supports complex physics: velocity-dependent advection, radiation, nonlinearities, and temperature dependencies that the user has defined in the setup of the FEM problem.

3. Built with accuracy at its core, our reduced models are designed to retain the essential physics of the full-order simulations.

- Difficulty in generating reduced models for real-time performance→Solved through advanced meshing, voxelization, and FEM tools that streamline model reduction while maintaining physical accuracy.

- High computational cost of full-scale simulations→Addressed by creating optimized, reduced-order models suitable for real-time execution.

- Discrepancies between simulated and real data→Minimized using discrepancy correction and AI-driven calibration.

- Limited scalability of traditional FEM approaches→Overcome by integrating data-driven and AI-based modeling to complement classical multiphysics FEM.

- Dependence on manual model tuning→Reduced through automated learning from high-quality, experience-based datasets that train neural networks for better accuracy and calibration.