Multiresolution convolutional autoencoders

Liu, Yuying; Ponce, Colin; Brunton, Steven L.; Kutz, J. Nathan

doi:10.1016/j.jcp.2022.111801

Cited by 13 publications

(6 citation statements)

References 33 publications

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“…Furthermore, AEs automatically learn relevant features from the data without the need for manual feature engineering which can save significant time and effort in pre-processing. This encourages the AEs to capture the crucial characteristics of the input data in its encoding, thereby learning a meaningful representation of the data in the latent code (Liu et al 2023).…”

Section: List Of Symbols Xmentioning

confidence: 99%

“…Beyond these applications feature learning, AEs foster a deeper understanding of data through the creation of meaningful representations. They also find practical utility in semantic embedding for NLP and information retrieval tasks and effectively reducing file sizes without compromising quality in image and signal compression (Liu et al 2023). Furthermore, AEs contribute to privacy preservation techniques, such as differential privacy, by protecting sensitive data while enabling analysis and insights.…”

Section: List Of Symbols Xmentioning

confidence: 99%

“…Furthermore, AEs contribute to privacy preservation techniques, such as differential privacy, by protecting sensitive data while enabling analysis and insights. In addition to these applications, AEs are instrumental in reducing data storage requirements, enhancing interpretability by revealing essential data features, and demonstrating robustness by generalizing well to new data and effectively handling noisy or incomplete data-sets (Liu et al 2023). Overall, AEs stand as versatile and indispensable tools, offering an extensive array of applications across diverse domains and problem types in machine learning and data analysis.…”

Section: List Of Symbols Xmentioning

confidence: 99%

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Autoencoders and their applications in machine learning: a survey

Berahmand,

Daneshfar,

Salehi

et al. 2024

Artif Intell Rev

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Autoencoders have become a hot researched topic in unsupervised learning due to their ability to learn data features and act as a dimensionality reduction method. With rapid evolution of autoencoder methods, there has yet to be a complete study that provides a full autoencoders roadmap for both stimulating technical improvements and orienting research newbies to autoencoders. In this paper, we present a comprehensive survey of autoencoders, starting with an explanation of the principle of conventional autoencoder and their primary development process. We then provide a taxonomy of autoencoders based on their structures and principles and thoroughly analyze and discuss the related models. Furthermore, we review the applications of autoencoders in various fields, including machine vision, natural language processing, complex network, recommender system, speech process, anomaly detection, and others. Lastly, we summarize the limitations of current autoencoder algorithms and discuss the future directions of the field.

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Section: List Of Symbols Xmentioning

confidence: 99%

Section: List Of Symbols Xmentioning

confidence: 99%

Section: List Of Symbols Xmentioning

confidence: 99%

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Autoencoders and their applications in machine learning: a survey

Berahmand,

Daneshfar,

Salehi

et al. 2024

Artif Intell Rev

View full text Add to dashboard Cite

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“…The model is first trained using only low-resolution flow data, and then, the pre-trained weights are transferred in training with high-resolution data sets. Transfer learning over multiple levels of spatial-resolution flow field can improve the accuracy of super-resolution reconstruction [93], which is also related to multi-fidelity learning [94]. U-Net-based model (illustrated in Fig.…”

Section: Supervised Learningmentioning

confidence: 99%

Super-resolution analysis via machine learning: a survey for fluid flows

Fukami

Fukagata

Taira

2023

Theor. Comput. Fluid Dyn.

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This paper surveys machine-learning-based super-resolution reconstruction for vortical flows. Super resolution aims to find the high-resolution flow fields from low-resolution data and is generally an approach used in image reconstruction. In addition to surveying a variety of recent super-resolution applications, we provide case studies of super-resolution analysis for an example of two-dimensional decaying isotropic turbulence. We demonstrate that physics-inspired model designs enable successful reconstruction of vortical flows from spatially limited measurements. We also discuss the challenges and outlooks of machine-learning-based super-resolution analysis for fluid flow applications. The insights gained from this study can be leveraged for super-resolution analysis of numerical and experimental flow data. Graphical abstract

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“…The classical POD [21,22] is only limited to linear projections of the data onto the compressed space, causing general loss of information in highly complex physics. AEs [23][24][25][26], which are the NN alternative to the POD, are a better alternative allowing a nonlinear compression and recovery. This work contributes generally to the question of dimensionality reduction of large datasets defined on irregular spatial domains.…”

Section: Introductionmentioning

confidence: 99%

Graph convolutional multi-mesh autoencoder for steady transonic aircraft aerodynamics

Massegur,

Da Ronch

2024

Mach. Learn.: Sci. Technol.

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Analysing the aerodynamic loads of an aircraft using computational fluid dynamics is a user's and computer-intensive task. An attractive alternative is to leverage on neural networks bypassing the need of solving the governing fluid equations at all flight conditions of interest. Neural networks have the ability to infer highly nonlinear predictions if a reference dataset is available. This work presents a geometric deep learning based multi-mesh autoencoder framework for steady-state transonic aerodynamics. The framework builds on graph neural networks which are designed for irregular and unstructured spatial discretisations, embedded in a multi-resolution algorithm for dimensionality reduction. The demonstration is for the NASA Common Research Model wing/body aircraft configuration. Thorough studies are presented discussing the model predictions in terms of vector fields, pressure and shear-stress coefficients, and scalar fields, total force and moment coefficients, for a range of nonlinear conditions involving shock waves and flow separation. We note that the cost of the model prediction is minimal, having used an existing available database.

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Multiresolution convolutional autoencoders

Cited by 13 publications

References 33 publications

Autoencoders and their applications in machine learning: a survey

Autoencoders and their applications in machine learning: a survey

Super-resolution analysis via machine learning: a survey for fluid flows

Graph convolutional multi-mesh autoencoder for steady transonic aircraft aerodynamics

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