Non-intrusive inference reduced order model for fluids using linear multistep neural network

Understand

In this effort we propose a data-driven learning framework for reduced order modeling of fluid dynamics.

• Designing accurate and efficient reduced order models for nonlinear fluid dynamic problems is challenging for many practical engineering applications.
• Classical projection-based model reduction methods generate reduced systems by projecting full-order differential operators into low-dimensional subspaces.
• However, these techniques usually lead to severe instabilities in the presence of highly nonlinear dynamics, which dramatically deteriorates the accuracy of the reduced-order models.