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

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 data-driven framework is proposed towards the end of predictive modeling of complex
spatio-temporal dynamics, leveraging nested non-linear manifolds. Three levels of neural …

This paper derives predictive reduced-order models for rocket engine combustion dynamics
via Operator Inference, a scientific machine learning approach that blends data-driven …

This paper presents a physics-based data-driven method to learn predictive reduced-order
models (ROMs) from high-fidelity simulations and illustrates it in the challenging context of a …

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 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 …

This work investigates the stability of (discrete) empirical interpolation for nonlinear model
reduction and state field approximation from measurements. Empirical interpolation derives …

Simulations of complex physical systems are typically realized by discretizing partial
differential equations (PDEs) on unstructured meshes. While neural networks have recently …

We present a new scientific machine learning method that learns from data a
computationally inexpensive surrogate model for predicting the evolution of a system …