Data-driven reduced order model with temporal convolutional neural network

Wu, Pin; Sun, Junwu; Chang, Xuting; Zhang, Wenjie; Arcucci, Rossella; Guo, Yike; Pain, Christopher C.

doi:10.1016/j.cma.2019.112766

Cited by 104 publications

(45 citation statements)

References 30 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…In the network environment, text expression has the characteristics of nonstandard, often using acronyms, network neologisms, spelling mistakes, grammar errors, and other problems, which brings great challenges to language processing. Methods to solve language processing problems mainly include dictionary-based methods, traditional machine learning methods, and deep learning methods [2][3][4].…”

Section: Introductionmentioning

confidence: 99%

[Retracted] Language Processing Model Construction and Simulation Based on Hybrid CNN and LSTM

Zhang

2021

Computational Intelligence and Neuroscience

View full text Add to dashboard Cite

Deep learning is the latest trend of machine learning and artificial intelligence research. As a new field with rapid development over the past decade, it has attracted more and more researchers’ attention. Convolutional Neural Network (CNN) model is one of the most important classical structures in deep learning models, and its performance has been gradually improved in deep learning tasks in recent years. Convolutional neural networks have been widely used in image classification, target detection, semantic segmentation, and natural language processing because they can automatically learn the feature representation of sample data. Firstly, this paper analyzes the model structure of a typical convolutional neural network model to increase the network depth and width in order to improve its performance, analyzes the network structure that further improves the model performance by using the attention mechanism, and then summarizes and analyzes the current special model structure. In order to further improve the text language processing effect, a convolutional neural network model, Hybrid convolutional neural network (CNN), and Long Short-Term Memory (LSTM) based on the fusion of text features and language knowledge are proposed. The text features and language knowledge are integrated into the language processing model, and the accuracy of the text language processing model is improved by parameter optimization. Experimental results on data sets show that the accuracy of the proposed model reaches 93.0%, which is better than the reference model in the literature.

show abstract

Section: Introductionmentioning

confidence: 99%

[Retracted] Language Processing Model Construction and Simulation Based on Hybrid CNN and LSTM

Zhang

2021

Computational Intelligence and Neuroscience

View full text Add to dashboard Cite

show abstract

“…The spatial resolution of the ERA5 reanalysis dataset is 0.25 • × 0.25 • . Studies have shown that wind speed, temperature, humidity, rainfall, and other factors have a certain impact on PM 2.5 concentration [35]. Referring to the existing research [27] and through experiments, this paper finally selected wind speed, boundary layer height, total amount of ozone column, boundary layer diffusion, temperature, evaporation, total precipitation, surface pressure, high-vegetation-cover index, low-vegetation-cover index, and relative humidity as auxiliary meteorological factors affecting PM 2.5 concentration.…”

Section: Weather Reanalysis Datamentioning

confidence: 99%

Estimation of PM2.5 Concentration Using Deep Bayesian Model Considering Spatial Multiscale

et al. 2021

View full text Add to dashboard Cite

Directly establishing the relationship between satellite data and PM2.5 concentration through deep learning methods for PM2.5 concentration estimation is an important means for estimating regional PM2.5 concentration. However, due to the lack of consideration of uncertainty in deep learning methods, methods based on deep learning have certain overfitting problems in the process of PM2.5 estimation. In response to this problem, this paper designs a deep Bayesian PM2.5 estimation model that takes into account multiple scales. The model uses a Bayesian neural network to describe key parameters a priori, provide regularization effects to the neural network, perform posterior inference through parameters, and take into account the characteristics of data uncertainty, which is used to alleviate the problem of model overfitting and to improve the generalization ability of the model. In addition, different-scale Moderate-Resolution Imaging Spectroradiometer (MODIS) satellite data and ERA5 reanalysis data were used as input to the model to strengthen the model’s perception of different-scale features of the atmosphere, as well as to further enhance the model’s PM2.5 estimation accuracy and generalization ability. Experiments with Anhui Province as the research area showed that the R2 of this method on the independent test set was 0.78, which was higher than that of the DNN, random forest, and BNN models that do not consider the impact of the surrounding environment; moreover, the RMSE was 19.45 μg·m−3, which was also lower than the three compared models. In the experiment of different seasons in 2019, compared with the other three models, the estimation accuracy was significantly reduced; however, the R2 of the model in this paper could still reach 0.66 or more. Thus, the model in this paper has a higher accuracy and better generalization ability.

show abstract

“…For example, convolutional autoencoders are gaining popularity to find the nonlinear basis functions of complex physical systems and they are complemented with the LSTM network for learning the latent-space dynamics. [70][71][72][73][74][75][76] The LSTM nudging framework can be easily applied to high dimensional systems, where convolutional autoencoders are employed for dimensionality reduction and the LSTM is trained to learn the nudging dynamics in latent-space instead of highdimensional space. Novel neural network architectures such as generative adversarial networks (GANs) 77,78 can also be applied to learn the nudging dynamics.…”

Section: B Long Short-term Memory Nudgingmentioning

confidence: 99%

Long short-term memory embedded nudging schemes for nonlinear data assimilation of geophysical flows

et al. 2020

View full text Add to dashboard Cite

Reduced rank nonlinear filters are increasingly utilized in data assimilation of geophysical flows but often require a set of ensemble forward simulations to estimate forecast covariance. On the other hand, predictor-corrector type nudging approaches are still attractive due to their simplicity of implementation when more complex methods need to be avoided. However, optimal estimate of the nudging gain matrix might be cumbersome. In this paper, we put forth a fully nonintrusive recurrent neural network approach based on a long shortterm memory (LSTM) embedding architecture to estimate the nudging term, which plays a role not only to force the state trajectories to the observations but also acts as a stabilizer. Furthermore, our approach relies on the power of archival data, and the trained model can be retrained effectively due to the power of transfer learning in any neural network applications. In order to verify the feasibility of the proposed approach, we perform twin experiments using the Lorenz 96 system. Our results demonstrate that the proposed LSTM nudging approach yields more accurate estimates than both the extended Kalman filter (EKF) and ensemble Kalman filter (EnKF) when only sparse observations are available. With the availability of emerging artificial intelligence friendly and modular hardware technologies and heterogeneous computing platforms, we articulate that our simplistic nudging framework turns out to be computationally more efficient than either the EKF or EnKF approaches.

show abstract

Data-driven reduced order model with temporal convolutional neural network

Cited by 104 publications

References 30 publications

[Retracted] Language Processing Model Construction and Simulation Based on Hybrid CNN and LSTM

[Retracted] Language Processing Model Construction and Simulation Based on Hybrid CNN and LSTM

Estimation of PM2.5 Concentration Using Deep Bayesian Model Considering Spatial Multiscale

Long short-term memory embedded nudging schemes for nonlinear data assimilation of geophysical flows

Contact Info

Product

Resources

About