Reduced-order modeling of unsteady fluid flow using neural network ensembles
- URL: http://arxiv.org/abs/2402.05372v2
- Date: Thu, 8 Aug 2024 19:11:58 GMT
- Title: Reduced-order modeling of unsteady fluid flow using neural network ensembles
- Authors: Rakesh Halder, Mohammadmehdi Ataei, Hesam Salehipour, Krzysztof Fidkowski, Kevin Maki,
- Abstract summary: We propose using bagging, a commonly used ensemble learning technique, to develop a fully data-driven reduced-order model framework.
The framework uses CAEs for spatial reconstruction of the full-order model and LSTM ensembles for time-series prediction.
Results show that the presented framework effectively reduces error propagation and leads to more accurate time-series prediction of latent variables at unseen points.
- Score: 0.0
- License: http://creativecommons.org/licenses/by/4.0/
- Abstract: The use of deep learning has become increasingly popular in reduced-order models (ROMs) to obtain low-dimensional representations of full-order models. Convolutional autoencoders (CAEs) are often used to this end as they are adept at handling data that are spatially distributed, including solutions to partial differential equations. When applied to unsteady physics problems, ROMs also require a model for time-series prediction of the low-dimensional latent variables. Long short-term memory (LSTM) networks, a type of recurrent neural network useful for modeling sequential data, are frequently employed in data-driven ROMs for autoregressive time-series prediction. When making predictions at unseen design points over long time horizons, error propagation is a frequently encountered issue, where errors made early on can compound over time and lead to large inaccuracies. In this work, we propose using bagging, a commonly used ensemble learning technique, to develop a fully data-driven ROM framework referred to as the CAE-eLSTM ROM that uses CAEs for spatial reconstruction of the full-order model and LSTM ensembles for time-series prediction. When applied to two unsteady fluid dynamics problems, our results show that the presented framework effectively reduces error propagation and leads to more accurate time-series prediction of latent variables at unseen points.
Related papers
- Temporal Subsampling Diminishes Small Spatial Scales in Recurrent Neural
Network Emulators of Geophysical Turbulence [0.0]
We investigate how an often overlooked processing step affects the quality of an emulator's predictions.
We implement ML architectures from a class of methods called reservoir computing: (1) a form of spatial Vector Autoregression (N VAR), and (2) an Echo State Network (ESN)
In all cases, subsampling the training data consistently leads to an increased bias at small scales that resembles numerical diffusion.
arXiv Detail & Related papers (2023-04-28T21:34:53Z) - Continuous time recurrent neural networks: overview and application to
forecasting blood glucose in the intensive care unit [56.801856519460465]
Continuous time autoregressive recurrent neural networks (CTRNNs) are a deep learning model that account for irregular observations.
We demonstrate the application of these models to probabilistic forecasting of blood glucose in a critical care setting.
arXiv Detail & Related papers (2023-04-14T09:39:06Z) - Online Evolutionary Neural Architecture Search for Multivariate
Non-Stationary Time Series Forecasting [72.89994745876086]
This work presents the Online Neuro-Evolution-based Neural Architecture Search (ONE-NAS) algorithm.
ONE-NAS is a novel neural architecture search method capable of automatically designing and dynamically training recurrent neural networks (RNNs) for online forecasting tasks.
Results demonstrate that ONE-NAS outperforms traditional statistical time series forecasting methods.
arXiv Detail & Related papers (2023-02-20T22:25:47Z) - A data filling methodology for time series based on CNN and (Bi)LSTM
neural networks [0.0]
We develop two Deep Learning models aimed at filling data gaps in time series obtained from monitored apartments in Bolzano, Italy.
Our approach manages to capture the fluctuating nature of the data and shows good accuracy in reconstructing the target time series.
arXiv Detail & Related papers (2022-04-21T09:40:30Z) - An advanced spatio-temporal convolutional recurrent neural network for
storm surge predictions [73.4962254843935]
We study the capability of artificial neural network models to emulate storm surge based on the storm track/size/intensity history.
This study presents a neural network model that can predict storm surge, informed by a database of synthetic storm simulations.
arXiv Detail & Related papers (2022-04-18T23:42:18Z) - Time Series Forecasting with Ensembled Stochastic Differential Equations
Driven by L\'evy Noise [2.3076895420652965]
We use a collection of SDEs equipped with neural networks to predict long-term trend of noisy time series.
Our contributions are, first, we use the phase space reconstruction method to extract intrinsic dimension of the time series data.
Second, we explore SDEs driven by $alpha$-stable L'evy motion to model the time series data and solve the problem through neural network approximation.
arXiv Detail & Related papers (2021-11-25T16:49:01Z) - CARRNN: A Continuous Autoregressive Recurrent Neural Network for Deep
Representation Learning from Sporadic Temporal Data [1.8352113484137622]
In this paper, a novel deep learning-based model is developed for modeling multiple temporal features in sporadic data.
The proposed model, called CARRNN, uses a generalized discrete-time autoregressive model that is trainable end-to-end using neural networks modulated by time lags.
It is applied to multivariate time-series regression tasks using data provided for Alzheimer's disease progression modeling and intensive care unit (ICU) mortality rate prediction.
arXiv Detail & Related papers (2021-04-08T12:43:44Z) - Anomaly Detection of Time Series with Smoothness-Inducing Sequential
Variational Auto-Encoder [59.69303945834122]
We present a Smoothness-Inducing Sequential Variational Auto-Encoder (SISVAE) model for robust estimation and anomaly detection of time series.
Our model parameterizes mean and variance for each time-stamp with flexible neural networks.
We show the effectiveness of our model on both synthetic datasets and public real-world benchmarks.
arXiv Detail & Related papers (2021-02-02T06:15:15Z) - Adjusting for Autocorrelated Errors in Neural Networks for Time Series
Regression and Forecasting [10.659189276058948]
We learn the autocorrelation coefficient jointly with the model parameters in order to adjust for autocorrelated errors.
For time series regression, large-scale experiments indicate that our method outperforms the Prais-Winsten method.
Results across a wide range of real-world datasets show that our method enhances performance in almost all cases.
arXiv Detail & Related papers (2021-01-28T04:25:51Z) - Deep Cellular Recurrent Network for Efficient Analysis of Time-Series
Data with Spatial Information [52.635997570873194]
This work proposes a novel deep cellular recurrent neural network (DCRNN) architecture to process complex multi-dimensional time series data with spatial information.
The proposed architecture achieves state-of-the-art performance while utilizing substantially less trainable parameters when compared to comparable methods in the literature.
arXiv Detail & Related papers (2021-01-12T20:08:18Z) - Liquid Time-constant Networks [117.57116214802504]
We introduce a new class of time-continuous recurrent neural network models.
Instead of declaring a learning system's dynamics by implicit nonlinearities, we construct networks of linear first-order dynamical systems.
These neural networks exhibit stable and bounded behavior, yield superior expressivity within the family of neural ordinary differential equations.
arXiv Detail & Related papers (2020-06-08T09:53:35Z)
This list is automatically generated from the titles and abstracts of the papers in this site.
This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.