Learning nonlinear reduced models from data with operator inference

B Kramer, B Peherstorfer… - Annual Review of Fluid …, 2024 - annualreviews.org
This review discusses Operator Inference, a nonintrusive reduced modeling approach that
incorporates physical governing equations by defining a structured polynomial form for the …

A survey of projection-based model reduction methods for parametric dynamical systems

P Benner, S Gugercin, K Willcox - SIAM review, 2015 - SIAM
Numerical simulation of large-scale dynamical systems plays a fundamental role in studying
a wide range of complex physical phenomena; however, the inherent large-scale nature of …

Projection-based model reduction: Formulations for physics-based machine learning

R Swischuk, L Mainini, B Peherstorfer, K Willcox - Computers & Fluids, 2019 - Elsevier
This paper considers the creation of parametric surrogate models for applications in science
and engineering where the goal is to predict high-dimensional output quantities of interest …

Reduced basis methods for time-dependent problems

JS Hesthaven, C Pagliantini, G Rozza - Acta Numerica, 2022 - cambridge.org
Numerical simulation of parametrized differential equations is of crucial importance in the
study of real-world phenomena in applied science and engineering. Computational methods …

Review of surrogate modeling in water resources

S Razavi, BA Tolson, DH Burn - Water Resources Research, 2012 - Wiley Online Library
Surrogate modeling, also called metamodeling, has evolved and been extensively used
over the past decades. A wide variety of methods and tools have been introduced for …

Model identification of reduced order fluid dynamics systems using deep learning

Z Wang, D Xiao, F Fang, R Govindan… - … Methods in Fluids, 2018 - Wiley Online Library
This paper presents a novel model reduction method: deep learning reduced order model,
which is based on proper orthogonal decomposition and deep learning methods. The deep …

Data-enabled physics-informed machine learning for reduced-order modeling digital twin: application to nuclear reactor physics

H Gong, S Cheng, Z Chen, Q Li - Nuclear Science and Engineering, 2022 - Taylor & Francis
This paper proposes an approach that combines reduced-order models with machine
learning in order to create physics-informed digital twins to predict high-dimensional output …

Parameter flexible wildfire prediction using machine learning techniques: Forward and inverse modelling

S Cheng, Y Jin, SP Harrison, C Quilodrán-Casas… - Remote Sensing, 2022 - mdpi.com
Parameter identification for wildfire forecasting models often relies on case-by-case tuning
or posterior diagnosis/analysis, which can be computationally expensive due to the …

Non-intrusive reduced order modeling of natural convection in porous media using convolutional autoencoders: comparison with linear subspace techniques

T Kadeethum, F Ballarin, Y Choi, D O'Malley… - Advances in Water …, 2022 - Elsevier
Natural convection in porous media is a highly nonlinear multiphysical problem relevant to
many engineering applications (eg, the process of CO 2 sequestration). Here, we extend …

Non‐intrusive reduced‐order modelling of the Navier–Stokes equations based on RBF interpolation

D Xiao, F Fang, C Pain, G Hu - International Journal for …, 2015 - Wiley Online Library
We present a new non‐intrusive model reduction method for the Navier–Stokes equations.
The method replaces the traditional approach of projecting the equations onto the reduced …