Neural Monte Carlo Fluid Simulation
Name
3641519.3657438.pdf
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67.21 MB
Format
Adobe PDF
Checksum (MD5)
092031aee908e624bb3da29003a8d867
Author(s)
Jain, Pranav
Qu, Ziyin
Chen, Peter Yichen
Stein, Oded
Date Issued
July 13, 2024
Publisher
ACM|Special Interest Group on Computer Graphics and Interactive Techniques Conference Conference Papers '24
Citation
Jain, Pranav, Qu, Ziyin, Chen, Peter Yichen and Stein, Oded. 2024. "Neural Monte Carlo Fluid Simulation."
Version
Final published version
Abstract
The idea of using a neural network to represent continuous vector fields (i.e., neural fields) has become popular for solving PDEs arising from physics simulations. Here, the classical spatial discretization (e.g., finite difference) of PDE solvers is replaced with a neural network that models a differentiable function, so the spatial gradients of the PDEs can be readily computed via autodifferentiation. When used in fluid simulation, however, neural fields fail to capture many important phenomena, such as the vortex shedding experienced in the von Kármán vortex street experiment. We present a novel neural network representation for fluid simulation that augments neural fields with explicitly enforced boundary conditions as well as a Monte Carlo pressure solver to get rid of all weakly enforced boundary conditions. Our method, the Neural Monte Carlo method (NMC), is completely mesh-free, i.e., it doesn’t depend on any grid-based discretization. While NMC does not achieve the state-of-the-art accuracy of the well-established grid-based methods, it significantly outperforms previous mesh-free neural fluid methods on fluid flows involving intricate boundaries and turbulence regimes.
MIT Department
Massachusetts Institute of Technology. Computer Science and Artificial Intelligence Laboratory
Terms of Use
Article is made available in accordance with the publisher's policy and may be subject to US copyright law. Please refer to the publisher's site for terms of use.
Persistent DSpace Link
DOI of Published Version
https://doi.org/10.1145/3641519.3657438
