We present a component-based model order reduction procedure to efficiently and
accurately solve parameterized incompressible flows governed by the Navier-Stokes …

Projection-based reduced order models (PROMs) have shown promise in representing the
behavior of multiscale systems using a small set of generalized (or latent) variables. Despite …

This paper focuses on the construction of accurate and predictive data-driven reduced
models of large-scale numerical simulations with complex dynamics and sparse training …

This paper formulates, analyzes and demonstrates numerically a method for the explicit
partitioned solution of coupled interface problems involving combinations of projection …

Online adaptive model reduction efficiently reduces numerical models of transport-
dominated problems by updating reduced spaces over time, which leads to nonlinear …

This work introduces reduced models based on Continuous Low Rank Adaptation
(CoLoRA) that pre-train neural networks for a given partial differential equation and then …

Many engineering applications rely on the evaluation of expensive, non-linear high-
dimensional functions. In this paper, we propose the RONAALP algorithm (Reduced Order …

The vast majority of reduced‐order models (ROMs) first obtain a low dimensional
representation of the problem from high‐dimensional model (HDM) training data which is …

This paper presents and evaluates a framework for the coupling of subdomain-local
projection-based reduced order models (PROMs) using the Schwarz alternating method …

We present a new framework towards the objective of learning coarse-grained models
based on the maximum entropy principle. We show that existing methods for assigning …