This review discusses Operator Inference, a nonintrusive reduced modeling approach that
incorporates physical governing equations by defining a structured polynomial form for the …

This review covers the new developments in machine learning (ML) that are impacting the
multi-disciplinary area of aerospace engineering, including fundamental fluid dynamics …

We develop a general framework for data-driven approximation of input-output maps
between infinitedimensional spaces. The proposed approach is motivated by the recent …

To overcome the limitations associated with purely physics-based and data-driven modeling
methods, hybrid, physics-based data-driven models have been developed, with improved …

This article addresses the inference of physics models from data, from the perspectives of
inverse problems and model reduction. These fields develop formulations that integrate data …

A quadratic approximation manifold is presented for performing nonlinear, projection-based,
model order reduction (PMOR). It constitutes a departure from the traditional affine subspace …

Enabling fast and accurate physical simulations with data has become an important area of
computational physics to aid in inverse problems, design-optimization, uncertainty …

An adaptive projection-based reduced-order model (ROM) formulation is presented for
model-order reduction of problems featuring chaotic and convection-dominant physics. An …

This paper proposes a novel approach for learning a data-driven quadratic manifold from
high-dimensional data, then employing this quadratic manifold to derive efficient physics …

This work proposes a Bayesian inference method for the reduced-order modeling of time-
dependent systems. Informed by the structure of the governing equations, the task of …