Core Concepts
Machine-learning-based ROMs offer flexibility and accuracy in reduced-order modeling.
Abstract
The content discusses the development of reduced-order models using Latent Space Dynamics Identification (LaSDI) algorithms. It explores the transformation of high-fidelity data into low-dimensional latent-space data governed by ordinary differential equations. Different LaSDI approaches are presented, including strategies to enforce thermodynamics laws, enhance robustness, select training data efficiently, and quantify prediction uncertainty. Performance demonstrations on various problems show significant speed-ups with relative errors of less than a few percent.
Stats
Recently, machine-learning-based ROMs have gained popularity.
LaSDI algorithms can achieve relative errors of less than a few percent.
Speed-ups of up to thousands of times have been demonstrated.
Quotes
"We demonstrate the performance of different LaSDI approaches on various examples."
"LaSDI algorithms can achieve relative errors of less than a few percent."