Adaptive learning‐based projection method for smoke simulation

Xiao, Xiangyun; Yang, Cheng; Yang, Xubo

doi:10.1002/cav.1837

Cited by 8 publications

(3 citation statements)

References 30 publications

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“…Ladický et al [LJS*15] proposed a fluid simulation method using Regression Forests and handcrafted features. Tomshon et al [TSSP17] and Xiao et al [XYY18] proposed DNN models that replace the pressure projection, which is a simulation stage with a high computational cost. Methods that generate visually enhanced flow simulations using DNNs have also been proposed.…”

Section: Related Workmentioning

confidence: 99%

Two‐step Temporal Interpolation Network Using Forward Advection for Efficient Smoke Simulation

Lee

2021

Computer Graphics Forum

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In this paper, we propose a two-step temporal interpolation network using forward advection to generate smoke simulation efficiently. By converting a low frame rate smoke simulation computed with a large time step into a high frame rate smoke simulation through inference of temporal interpolation networks, the proposed method can efficiently generate smoke simulation with a high frame rate and low computational costs. The first step of the proposed method is optical flow-based temporal interpolation using deep neural networks (DNNs) for two given smoke animation frames. In the next step, we compute temporary smoke frames with forward advection, a physical computation with a low computational cost. We then interpolate between the results of the forward advection and those of the first step to generate more accurate and enhanced interpolated results. We performed quantitative analyses of the results generated by the proposed method and previous temporal interpolation methods. Furthermore, we experimentally compared the performance of the proposed method with previous methods using DNNs for smoke simulation. We found that the results generated by the proposed method are more accurate and closer to the ground truth smoke simulation than those generated by the previous temporal interpolation methods. We also confirmed that the proposed method generates smoke simulation results more efficiently with lower computational costs than previous smoke simulation methods using DNNs.

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Section: Related Workmentioning

confidence: 99%

Two‐step Temporal Interpolation Network Using Forward Advection for Efficient Smoke Simulation

Lee

2021

Computer Graphics Forum

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“…Ladick'y et al [13] proposed a fluid simulation method based on Regression Forests and handcrafted features. Tompson et al [24] and Xiao et al [28] used the DNN models to replace the pressure projection, which is an expensive computational cost simulation stage. In addition, some work has made good progress in encoding fluid simulation into simplified representations.…”

Section: Related Workmentioning

confidence: 99%

Resistance-Time Co-Modulated PointNet for Temporal Super-Resolution Simulation of Blood Vessel Flows

Jiang¹,

Gao²,

Gu³

et al. 2021

Preprint

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In this paper, a novel deep learning framework is proposed for temporal super-resolution simulation of blood vessel flows, in which a high-temporal-resolution timevarying blood vessel flow simulation is generated from a low-temporal-resolution flow simulation result. In our framework, point-cloud is used to represent the complex blood vessel model, resistance-time aided PointNet model is proposed for extracting the time-space features of the time-varying flow field, and finally we can reconstruct the high-accuracy and high-resolution flow field through the Decoder module. In particular, the amplitude loss and the orientation loss of the velocity are proposed from the vector characteristics of the velocity. And the combination of these two metrics constitutes the final loss function for network training. Several examples are given to illustrate the effective and efficiency of the proposed framework for temporal super-resolution simulation of blood vessel flows.

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“…Huang et al [HMK11] presented a method maintained the main coarse-grid features by ex- employed a CNN to handle the whole grids' divergence. Xiao et al [XYY18] introduced an adaptive machine learning method to improve the prediction accuracy and stability of the Poisson solver based on a neural network. They mainly focused on the projection step acceleration.…”

Section: Related Workmentioning

confidence: 99%

A CNN‐based Flow Correction Method for Fast Preview

Xiao

Wang

Yang

2019

Computer Graphics Forum

Self Cite

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Eulerian‐based smoke simulations are sensitive to the initial parameters and grid resolutions. Due to the numerical dissipation on different levels of the grid and the nonlinearity of the governing equations, the differences in simulation resolutions will result in different results. This makes it challenging for artists to preview the animation results based on low‐resolution simulations. In this paper, we propose a learning‐based flow correction method for fast previewing based on low‐resolution smoke simulations. The main components of our approach lie in a deep convolutional neural network, a grid‐layer feature vector and a special loss function. We provide a novel matching model to represent the relationship between low‐resolution and high‐resolution smoke simulations and correct the overall shape of a low‐resolution simulation to closely follow the shape of a high‐resolution down‐sampled version. We introduce the grid‐layer concept to effectively represent the 3D fluid shape, which can also reduce the input and output dimensions. We design a special loss function for the fluid divergence‐free constraint in the neural network training process. We have demonstrated the efficacy and the generality of our approach by simulating a diversity of animations deviating from the original training set. In addition, we have integrated our approach into an existing fluid simulation framework to showcase its wide applications.

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Adaptive learning‐based projection method for smoke simulation

Cited by 8 publications

References 30 publications

Two‐step Temporal Interpolation Network Using Forward Advection for Efficient Smoke Simulation

Two‐step Temporal Interpolation Network Using Forward Advection for Efficient Smoke Simulation

Resistance-Time Co-Modulated PointNet for Temporal Super-Resolution Simulation of Blood Vessel Flows

A CNN‐based Flow Correction Method for Fast Preview

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