Neural operator learning of heterogeneous mechanobiological insults contributing to aortic aneurysms

Goswami, Somdatta; Li, David S.; Rego, Bruno V.; Latorre, Marcos; Humphrey, Jay D.; Karniadakis, George Em

doi:10.1098/rsif.2022.0410

Cited by 20 publications

(8 citation statements)

References 48 publications

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“…Lu et al., 2021 proposed a neural operator network, DeepONet, which achieved a leap from the general approximation theory of neural operators to practice. Subsequently, DeepONet has been applied in classical fields such as life science (Goswami et al., 2022), material science (Oommen et al., 2022)and physics (Cai et al., 2020; Leoni et al., 2021; Yin et al., 2022). DeepONet is composed of the branch net and the trunk net.…”

Section: Methodsmentioning

confidence: 99%

High‐Precision and Fast Prediction of Regional Wind Fields in Near Space Using Neural‐Network Approximation of Operators

Chen,

Sheng,

2023

Geophysical Research Letters

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Fine modeling and fast prediction of regional wind field in the middle and upper atmosphere has always been a difficult problem. We designed a neural operator method to solve this problem. We combine the idea of data assimilation with deep learning method to design a regional wind field operator suitable for near space. The annual Root mean square error of the zonal wind and meridional wind of the operator model at the height of 30 km are 0.903 and 0.881, respectively, which is three times that of ConvLSTM. Moreover, we validate the sparse spatio‐temporal modeling method of regional wind field operator at 20/30/40/50 km altitude. The result shows that the model is mesh‐free, and can get high‐precision modeling of different spatio‐temporal resolutions, multiple regions and arbitrary positions at one time, which lays an foundation for fine regional modeling and rapid utilization of near space.

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Section: Methodsmentioning

confidence: 99%

High‐Precision and Fast Prediction of Regional Wind Fields in Near Space Using Neural‐Network Approximation of Operators

Chen,

Sheng,

2023

Geophysical Research Letters

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“…We have implemented three operator networks that have shown promising results so far, the DeepONet [17], the Fourier neural operator (FNO) [18], and the Wavelet neural operator (WNO) [20]. Although the original DeepONet architecture proposed in [17] has shown remarkable success, several extensions have been proposed in [24][25][26] to modify its implementation and produce efficient and robust architectures. The architectures of DeepONet, FNO, and WNO are shown in Fig.…”

Section: Neural Operatorsmentioning

confidence: 99%

Deep neural operators can predict the real-time response of floating offshore structures under irregular waves

Cao¹,

Goswami²,

Karniadakis³

et al. 2023

Preprint

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The use of neural operators in a digital twin model of an offshore floating structure can provide a paradigm shift in structural response prediction and health monitoring, providing valuable information for real-time control. In this work, the performance of three neural operators is evaluated, namely, deep operator network (DeepONet), Fourier neural operator (FNO), and Wavelet neural operator (WNO). We investigate the effectiveness of the operators to accurately capture the responses of a floating structure under six different sea state codes (3 − 8) based on the wave characteristics described by the World Meteorological Organization (WMO). The results demonstrate that these high-precision neural operators can deliver structural responses more efficiently, up to two orders of magnitude faster than a dynamic analysis using conventional numerical solvers. Additionally, compared to gated recurrent units (GRUs), a commonly used recurrent neural network for time-series estimation, neural operators are both more accurate and efficient, especially in situations with limited data availability. To further enhance the accuracy, novel extensions, such as wavelet-DeepONet and self-adaptive WNO, are proposed. Taken together, our study shows that FNO outperforms all other operators for approximating the mapping of one input functional space to the output space as well as for responses that have small bandwidth of the frequency spectrum, whereas for learning the mapping of multiple functions in the input space to the output space as well as for capturing responses within a large frequency spectrum, DeepONet with historical states provides the highest accuracy.

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“…Fortunately, scientific machine learning is a new research paradigm emerging in recent years, and it is also an important frontier cross field between AI and basic science. It has accelerated the knowledge discovery and efficient scientific computing of complex physical scenes, and has developed rapidly in physics, chemistry, life science and other fields (Goswami et al., 2022; Leoni et al., 2021; Yin et al., 2022). Lu et al.…”

Section: Introductionmentioning

confidence: 99%

Refined Short‐Term Forecasting Atmospheric Temperature Profiles in the Stratosphere Based on Operators Learning of Neural Networks

Chen,

Sheng,

Cui

2024

Earth and Space Science

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The efficacious forecasting of single‐station atmospheric temperature profiles can provide essential support for the structural design and flight missions of spacecrafts in near space. However, empirical models and reference atmospheric models most are calculations of the average state of the atmosphere profiles. Numerical assimilation models require expensive computational costs to improve the accuracy for medium and long‐term forecasting. It has been still a challenge to refined predict short‐term temperature profiles of near space at a low‐cost. We present a temperature profile operator method for refined modeling in the stratosphere by fusing the ability of Long Short‐Term Memory (LSTM) networks or its variants‐ bidirectional LSTM (BiLSTM) to exploit time series correlated information and deep operator networks (DeepONets) to approximate the solution operator of temperature profiles. It consists of three subnetworks. The first subnetwork is used to approximate the discrete temperature profile function, the second net is applied to represent the spatial information of pressure heights, and the third branch is utilized to encode the time domain of the temperature profile operator. We first use the hourly low latitude temperature data (20–50 km) from ERA5 for training, verification and iterative testing in the next 48 hr. The results denote that the temperature profile operator network has a stable and low error of cumulative generalization, and the BiLSTM operator significantly outperform the other models. We also apply two scenarios to testing the refined applicability of the high latitude temperature profile operator and the mid latitude wind profile operator in the stratosphere. This work provides a novel perspective for us to study the refined single‐station modeling of the upper and middle atmosphere.

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Neural operator learning of heterogeneous mechanobiological insults contributing to aortic aneurysms

Cited by 20 publications

References 48 publications

High‐Precision and Fast Prediction of Regional Wind Fields in Near Space Using Neural‐Network Approximation of Operators

High‐Precision and Fast Prediction of Regional Wind Fields in Near Space Using Neural‐Network Approximation of Operators

Deep neural operators can predict the real-time response of floating offshore structures under irregular waves

Refined Short‐Term Forecasting Atmospheric Temperature Profiles in the Stratosphere Based on Operators Learning of Neural Networks

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