Machine learning methods for nonlinear dimensionality reduction of the thermospheric density field

Nateghi, Vahid; Manzi, Matteo

doi:10.1016/j.asr.2023.08.010

Cited by 5 publications

(1 citation statement)

References 16 publications

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“…Nonlinear dimensionality reduction methods, on the other hand, assume that data may contain nonlinear structures during the dimensionality reduction process, allowing for more accurate capture of complex relationships within the The higher dimensionality of the flight parameter load strains makes it more difficult to build the prediction model, Silva et al [15] suggested that the dimensionality of the obtained flight parameters should be reduced while retaining some important dimensions. Common dimensionality reduction methods include Principal Component Analysis (PCA), AE, t-Distributed Stochastic Neighbor Embedding (t-SNE), and Locally Linear Embedding (LLE) [16], where PCA belongs to linear dimensionality reduction methods [17], while AE, t-SNE, and LLE belong to nonlinear dimensionality reduction methods [18]. Linear dimensionality reduction methods assume that data can be represented by linear transformations during the dimensionality reduction process, where the reduced features are linear combinations of the original features.…”

Section: Introductionmentioning

confidence: 99%

The Research on Deep Learning-Driven Dimensionality Reduction and Strain Prediction Techniques Based on Flight Parameter Data

Huang,

Wang,

Zhang

et al. 2024

Applied Sciences

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Loads and strains in critical areas play a crucial role in aircraft structural health monitoring, the tracking of individual aircraft lifespans, and the compilation of load spectra. Direct measurement of actual flight loads presents challenges. This process typically involves using load-strain stiffness matrices, derived from ground calibration tests, to map measured flight parameters to loads at critical locations. Presently, deep learning neural network methods are rapidly developing, offering new perspectives for this task. This paper explores the potential of deep learning models in predicting flight parameter loads and strains, integrating the methods of flight parameter preprocessing techniques, flight maneuver recognition (FMR), virtual ground calibration tests for wings, dimensionality reduction of flight data through Autoencoder (AE) network models, and the application of Long Short-Term Memory (LSTM) network models to predict strains. These efforts contribute to the prediction of strains in critical areas based on flight parameters, thereby enabling real-time assessment of aircraft damage.

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Section: Introductionmentioning

confidence: 99%

The Research on Deep Learning-Driven Dimensionality Reduction and Strain Prediction Techniques Based on Flight Parameter Data

Huang,

Wang,

Zhang

et al. 2024

Applied Sciences

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Why topological data analysis detects financial bubbles?

Akingbade,

Gidea,

Manzi

et al. 2024

Communications in Nonlinear Science and Numerical Simulation

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Reduced Order Probabilistic Emulation for Physics‐Based Thermosphere Models

Licata

Mehta

2023

Space Weather

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The geospace environment is volatile and highly driven. Space weather has effects on Earth's magnetosphere that cause a dynamic and enigmatic response in the thermosphere, particularly on the evolution of neutral mass density. Many models exist that use space weather drivers to produce a density response, but these models are typically computationally expensive or inaccurate for certain space weather conditions. In response, this work aims to employ a probabilistic machine learning (ML) method to create an efficient surrogate for the Thermosphere Ionosphere Electrodynamics General Circulation Model (TIE‐GCM), a physics‐based thermosphere model. Our method leverages principal component analysis to reduce the dimensionality of TIE‐GCM and recurrent neural networks to model the dynamic behavior of the thermosphere much quicker than the numerical model. The newly developed reduced order probabilistic emulator (ROPE) uses Long‐Short Term Memory neural networks to perform time‐series forecasting in the reduced state and provide distributions for future density. We show that across the available data, TIE‐GCM ROPE has similar error to previous linear approaches while improving storm‐time modeling. We also conduct a satellite propagation study for the significant November 2003 storm which shows that TIE‐GCM ROPE can capture the position resulting from TIE‐GCM density with <5 km bias. Simultaneously, linear approaches provide point estimates that can result in biases of 7–18 km.

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Machine learning methods for nonlinear dimensionality reduction of the thermospheric density field

Cited by 5 publications

References 16 publications

The Research on Deep Learning-Driven Dimensionality Reduction and Strain Prediction Techniques Based on Flight Parameter Data

The Research on Deep Learning-Driven Dimensionality Reduction and Strain Prediction Techniques Based on Flight Parameter Data

Why topological data analysis detects financial bubbles?

Reduced Order Probabilistic Emulation for Physics‐Based Thermosphere Models

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