Generalization capabilities and robustness of hybrid machine learning models grounded in flow physics compared to purely deep learning models
- URL: http://arxiv.org/abs/2404.17884v2
- Date: Mon, 28 Oct 2024 15:31:11 GMT
- Title: Generalization capabilities and robustness of hybrid machine learning models grounded in flow physics compared to purely deep learning models
- Authors: Rodrigo Abadía-Heredia, Adrián Corrochano, Manuel Lopez-Martin, Soledad Le Clainche,
- Abstract summary: This study investigates the generalization capabilities and robustness of purely deep learning (DL) models and hybrid models based on physical principles in fluid dynamics applications.
Three autoregressive models were compared: a convolutional autoencoder combined with a convolutional LSTM, a variational autoencoder (VAE) combined with a ConvLSTM and a hybrid model that combines proper decomposition (POD) with a LSTM (POD-DL)
While the VAE and ConvLSTM models accurately predicted laminar flow, the hybrid POD-DL model outperformed the others across both laminar and turbulent flow regimes.
- Score: 2.8686437689115363
- License:
- Abstract: This study investigates the generalization capabilities and robustness of purely deep learning (DL) models and hybrid models based on physical principles in fluid dynamics applications, specifically focusing on iteratively forecasting the temporal evolution of flow dynamics. Three autoregressive models were compared: a convolutional autoencoder combined with a convolutional LSTM (ConvLSTM), a variational autoencoder (VAE) combined with a ConvLSTM and a hybrid model that combines proper orthogonal decomposition (POD) with a LSTM (POD-DL). These models were tested on two high-dimensional, nonlinear datasets representing the velocity field of flow past a circular cylinder in both laminar and turbulent regimes. The study used latent dimension methods, enabling a bijective reduction of high-dimensional dynamics into a lower-order space to facilitate future predictions. While the VAE and ConvLSTM models accurately predicted laminar flow, the hybrid POD-DL model outperformed the others across both laminar and turbulent flow regimes. This success is attributed to the model's ability to incorporate modal decomposition, reducing the dimensionality of the data, by a non-parametric method, and simplifying the forecasting component. By leveraging POD, the model not only gained insight into the underlying physics, improving prediction accuracy with less training data, but also reduce the number of trainable parameters as POD is non-parametric. The findings emphasize the potential of hybrid models, particularly those integrating modal decomposition and deep learning, in predicting complex flow dynamics.
Related papers
- Koopman-Based Surrogate Modelling of Turbulent Rayleigh-Bénard Convection [4.248022697109535]
We use a Koopman-inspired architecture called the Linear Recurrent Autoencoder Network (LRAN) for learning reduced-order dynamics in convection flows.
A traditional fluid dynamics method, the Kernel Dynamic Mode Decomposition (KDMD) is used to compare the LRAN.
We obtained more accurate predictions with the LRAN than with KDMD in the most turbulent setting.
arXiv Detail & Related papers (2024-05-10T12:15:02Z) - Towards Theoretical Understandings of Self-Consuming Generative Models [56.84592466204185]
This paper tackles the emerging challenge of training generative models within a self-consuming loop.
We construct a theoretical framework to rigorously evaluate how this training procedure impacts the data distributions learned by future models.
We present results for kernel density estimation, delivering nuanced insights such as the impact of mixed data training on error propagation.
arXiv Detail & Related papers (2024-02-19T02:08:09Z) - Data-driven Nonlinear Model Reduction using Koopman Theory: Integrated
Control Form and NMPC Case Study [56.283944756315066]
We propose generic model structures combining delay-coordinate encoding of measurements and full-state decoding to integrate reduced Koopman modeling and state estimation.
A case study demonstrates that our approach provides accurate control models and enables real-time capable nonlinear model predictive control of a high-purity cryogenic distillation column.
arXiv Detail & Related papers (2024-01-09T11:54:54Z) - Online Variational Sequential Monte Carlo [49.97673761305336]
We build upon the variational sequential Monte Carlo (VSMC) method, which provides computationally efficient and accurate model parameter estimation and Bayesian latent-state inference.
Online VSMC is capable of performing efficiently, entirely on-the-fly, both parameter estimation and particle proposal adaptation.
arXiv Detail & Related papers (2023-12-19T21:45:38Z) - Capturing dynamical correlations using implicit neural representations [85.66456606776552]
We develop an artificial intelligence framework which combines a neural network trained to mimic simulated data from a model Hamiltonian with automatic differentiation to recover unknown parameters from experimental data.
In doing so, we illustrate the ability to build and train a differentiable model only once, which then can be applied in real-time to multi-dimensional scattering data.
arXiv Detail & Related papers (2023-04-08T07:55:36Z) - Forecasting through deep learning and modal decomposition in two-phase
concentric jets [2.362412515574206]
This work aims to improve fuel chamber injectors' performance in turbofan engines.
It requires the development of models that allow real-time prediction and improvement of the fuel/air mixture.
arXiv Detail & Related papers (2022-12-24T12:59:41Z) - Data-driven low-dimensional dynamic model of Kolmogorov flow [0.0]
Reduced order models (ROMs) that capture flow dynamics are of interest for decreasing computational costs for simulation.
This work presents a data-driven framework for minimal-dimensional models that effectively capture the dynamics and properties of the flow.
We apply this to Kolmogorov flow in a regime consisting of chaotic and intermittent behavior.
arXiv Detail & Related papers (2022-10-29T23:05:39Z) - Data-driven Control of Agent-based Models: an Equation/Variable-free
Machine Learning Approach [0.0]
We present an Equation/Variable free machine learning (EVFML) framework for the control of the collective dynamics of complex/multiscale systems.
The proposed implementation consists of three steps: (A) from high-dimensional agent-based simulations, machine learning (in particular, non-linear manifold learning (DMs))
We exploit the Equation-free approach to perform numerical bifurcation analysis of the emergent dynamics.
We design data-driven embedded wash-out controllers that drive the agent-based simulators to their intrinsic, imprecisely known, emergent open-loop unstable steady-states.
arXiv Detail & Related papers (2022-07-12T18:16:22Z) - Realization of the Trajectory Propagation in the MM-SQC Dynamics by
Using Machine Learning [4.629634111796585]
We apply the supervised machine learning (ML) approach to realize the trajectory-based nonadiabatic dynamics.
The proposed idea is proven to be reliable and accurate in the simulations of the dynamics of several site-exciton electron-phonon coupling models.
arXiv Detail & Related papers (2022-07-11T01:23:36Z) - Learning Large-scale Subsurface Simulations with a Hybrid Graph Network
Simulator [57.57321628587564]
We introduce Hybrid Graph Network Simulator (HGNS) for learning reservoir simulations of 3D subsurface fluid flows.
HGNS consists of a subsurface graph neural network (SGNN) to model the evolution of fluid flows, and a 3D-U-Net to model the evolution of pressure.
Using an industry-standard subsurface flow dataset (SPE-10) with 1.1 million cells, we demonstrate that HGNS is able to reduce the inference time up to 18 times compared to standard subsurface simulators.
arXiv Detail & Related papers (2022-06-15T17:29:57Z) - Hybrid Physics and Deep Learning Model for Interpretable Vehicle State
Prediction [75.1213178617367]
We propose a hybrid approach combining deep learning and physical motion models.
We achieve interpretability by restricting the output range of the deep neural network as part of the hybrid model.
The results show that our hybrid model can improve model interpretability with no decrease in accuracy compared to existing deep learning approaches.
arXiv Detail & Related papers (2021-03-11T15:21:08Z)
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.