Learning physics-based models from data: perspectives from inverse problems and model reduction
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 …
inverse problems and model reduction. These fields develop formulations that integrate data …
Multi-level convolutional autoencoder networks for parametric prediction of spatio-temporal dynamics
J Xu, K Duraisamy - Computer Methods in Applied Mechanics and …, 2020 - Elsevier
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 …
spatio-temporal dynamics, leveraging nested non-linear manifolds. Three levels of neural …
Data-driven reduced-order models via regularised operator inference for a single-injector combustion process
This paper derives predictive reduced-order models for rocket engine combustion dynamics
via Operator Inference, a scientific machine learning approach that blends data-driven …
via Operator Inference, a scientific machine learning approach that blends data-driven …
Nonlinear model order reduction via lifting transformations and proper orthogonal decomposition
B Kramer, KE Willcox - AIAA Journal, 2019 - arc.aiaa.org
This paper presents a structure-exploiting nonlinear model reduction method for systems
with general nonlinearities. First, the nonlinear model is lifted to a model with more structure …
with general nonlinearities. First, the nonlinear model is lifted to a model with more structure …
Learning physics-based reduced-order models for a single-injector combustion process
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 …
models (ROMs) from high-fidelity simulations and illustrates it in the challenging context of a …
Bayesian operator inference for data-driven reduced-order modeling
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 …
dependent systems. Informed by the structure of the governing equations, the task of …
Model reduction for multi-scale transport problems using model-form preserving least-squares projections with variable transformation
A projection-based formulation is presented for non-linear model reduction of problems with
extreme scale disparity. The approach allows for the selection of an arbitrary, but complete …
extreme scale disparity. The approach allows for the selection of an arbitrary, but complete …
Non-intrusive data-driven model reduction for differential–algebraic equations derived from lifting transformations
P Khodabakhshi, KE Willcox - Computer Methods in Applied Mechanics …, 2022 - Elsevier
This paper presents a non-intrusive data-driven approach for model reduction of nonlinear
systems. The approach considers the particular case of nonlinear partial differential …
systems. The approach considers the particular case of nonlinear partial differential …
Investigations and improvement of robustness of reduced-order models of reacting flow
The impact of chemical reactions on the robustness and accuracy of projection-based
reduced-order models (ROMs) of fluid flows is investigated. Both Galerkin and least squares …
reduced-order models (ROMs) of fluid flows is investigated. Both Galerkin and least squares …
A predictive surrogate model for hemodynamics and structural prediction in abdominal aorta for different physiological conditions
X Tang, CJ Wu - Computer Methods and Programs in Biomedicine, 2024 - Elsevier
Background and objective This study investigates the application of a Predictive Surrogate
Model (PSM) for the prediction of the fluid and solid variables in the abdominal aorta by …
Model (PSM) for the prediction of the fluid and solid variables in the abdominal aorta by …