Convolutional Neural Networks for Steady Flow Approximation

By Xiaoxiao Guo, Francesco Iorio. Knowledge Discovery and Data Mining, 2016

911 citations recorded by the source corpus.

Subfields: Computer Vision and Pattern Recognition, Statistical and Nonlinear Physics

View Paper: https://www.semanticscholar.org/paper/49252c023bf47bb6f6b0b8baebb11f489750262d

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In Computer Vision and Pattern Recognition

In Statistical and Nonlinear Physics

Citations in the corpus

Deep Generative Design: Integration of Topology Optimization and Generative Models (2019, methodology): “[10] propose the CNN model to predict the responses of CFD simulation for the 2D shape of an automobile, and Tompson et al.”
Learning Mesh-Based Simulation with Graph Networks (2020, background): “High resolution simulations are often very slow, and learned models can provide faster predictions, reducing turnaround time for workflows in engineering and science [14, 6, 44, 18, 1].”
Fourier Neural Operator for Parametric Partial Differential Equations (2020, background): “These approaches parameterize the solution operator as a deep convolutional neural network between ﬁnite-dimensional Euclidean spaces Guo et al. (2016); Zhu & Zabaras (2018); Adler & Oktem (2017); Bhatnagar et al. (2019); Khoo et al. (2017).”
Choose a Transformer: Fourier or Galerkin (2021, methodology): “Deep convolutional neural networks (DCNN) are widely applied to learn the solution maps with a fixed discretization size [1, 9, 40, 36, 35, 100, 86].”
Neural Operator: Learning Maps Between Function Spaces (2021, positive): “It remedies the mesh-dependent nature of the approach in the papers (Guo et al., 2016; Zhu and Zabaras, 2018; Adler and Oktem, 2017; Bhatnagar et al., 2019) by producing a single set of network parameters that can be used with different discretizations.”
Integrating Physics-Based Modeling with Machine Learning: A Survey (2020, neutral): “by objective and method Basic ML Physics-Guided Loss Function Physics-Guided Initialization Physics-Guided Architecture Residual Model Hybrid Model Improve prediction beyond physical model [287] [35] [102] [104] [271] [247] [4] [199] [139][128] [160] [183] [66] [74] [119] [153] [182] [213] [295] [10”
RMA: Rapid Motor Adaptation for Legged Robots (2021, methodology): “optimization problems with their solutions, we could use a neural network to approximate the function mapping the problem to its solution [1, 17].”
Multipole Graph Neural Operator for Parametric Partial Differential Equations (2020, background): “The first parametrizes the solution operator as a deep convolutional neural network (CNN) between finite-dimensional Euclidean spaces Fθ : R → R [23, 49, 3, 9, 45].”
Multiwavelet-based Operator Learning for Differential Equations (2021, background): “The FNO model with higher values of k m has better performance due to more Fourier bases for representing the high-frequency signal, while MWT does better even with low modes in its A, B, C CNNs, highlighting the importance of using wavelet-based ﬁlters in the signal processing.”
Deep Fluids: A Generative Network for Parameterized Fluid Simulations (2018, methodology): “Lattice-Boltzmann steady-state ﬂow solutions are recovered by CNN surro-gates using signed distance functions as input boundary conditions in [GLI16].”
Data-driven prediction of unsteady flow over a circular cylinder using deep learning (2018, methodology): “[23] employed a CNN to predict steady flow fields around bluff objects and reported reasonable predictions with significantly reduced computational cost than that required for numerical simulations; Miyanawala and Jaiman [24] employed a CNN to predict aerodynamic force coefficients of bluff bodies, ”
Prediction of aerodynamic flow fields using convolutional neural networks (2019, methodology): “[12] reported the analysis and prediction of non-uniform steady laminar flow fields around bluff body objects by employing a convolutional neural network (CNN).”
Solver-in-the-Loop: Learning from Differentiable Physics to Interact with Iterative PDE-Solvers (2020, background): “For example, both steady [19] and unsteady [40], as well as multi-phase flows [16] have been investigated with deep learning based approaches.”