A 3D Machine Learning based Volume Of Fluid scheme without explicit interface reconstruction

• URL: http://arxiv.org/abs/2507.05218v1
• Date: Mon, 07 Jul 2025 17:30:00 GMT
• Title: A 3D Machine Learning based Volume Of Fluid scheme without explicit interface reconstruction
• Authors: Moreno Pintore, Bruno Després,
• Abstract summary: We present a machine-learning based Volume Of Fluid method to simulate multi-material flows on three-dimensional domains.<n>One of the novelties of the method is that the flux fraction is computed by evaluating a previously trained neural network.<n>We observe numerical convergence as the size of the mesh tends to zero $h=1/N_hsearrow 0$, with a better rate than two reference schemes.
• Score: 0.0
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: We present a machine-learning based Volume Of Fluid method to simulate multi-material flows on three-dimensional domains. One of the novelties of the method is that the flux fraction is computed by evaluating a previously trained neural network and without explicitly reconstructing any local interface approximating the exact one. The network is trained on a purely synthetic dataset generated by randomly sampling numerous local interfaces and which can be adapted to improve the scheme on less regular interfaces when needed. Several strategies to ensure the efficiency of the method and the satisfaction of physical constraints and properties are suggested and formalized. Numerical results on the advection equation are provided to show the performance of the method. We observe numerical convergence as the size of the mesh tends to zero $h=1/N_h\searrow 0$, with a better rate than two reference schemes.

Related papers

• Fusing CFD and measurement data using transfer learning [49.1574468325115]
  We introduce a non-linear method based on neural networks combining simulation and measurement data via transfer learning.<n>In a first step, the neural network is trained on simulation data to learn spatial features of the distributed quantities.<n>The second step involves transfer learning on the measurement data to correct for systematic errors between simulation and measurement by only re-training a small subset of the entire neural network model.
  arXiv  Detail & Related papers  (2025-07-28T07:21:46Z)
• 3DMeshNet: A Three-Dimensional Differential Neural Network for Structured Mesh Generation [2.892556380266997]
  We propose a novel method, 3DMeshNet, for three-dimensional structured mesh generation. 3DMeshNet embeds the meshing-related differential equations into the loss function of neural networks. It can efficiently output a three-dimensional structured mesh with a user-defined number of quadrilateral/hexahedral cells.
  arXiv  Detail & Related papers  (2024-05-07T13:07:07Z)
• Solving the Discretised Multiphase Flow Equations with Interface Capturing on Structured Grids Using Machine Learning Libraries [0.6299766708197884]
  This paper solves the discretised multiphase flow equations using tools and methods from machine-learning libraries. For the first time, finite element discretisations of multiphase flows can be solved using an approach based on (untrained) convolutional neural networks.
  arXiv  Detail & Related papers  (2024-01-12T18:42:42Z)
• Neural Vector Fields: Implicit Representation by Explicit Learning [63.337294707047036]
  We propose a novel 3D representation method, Neural Vector Fields (NVF) It not only adopts the explicit learning process to manipulate meshes directly, but also the implicit representation of unsigned distance functions (UDFs) Our method first predicts displacement queries towards the surface and models shapes as text reconstructions.
  arXiv  Detail & Related papers  (2023-03-08T02:36:09Z)
• NeuralMeshing: Differentiable Meshing of Implicit Neural Representations [63.18340058854517]
  We propose a novel differentiable meshing algorithm for extracting surface meshes from neural implicit representations. Our method produces meshes with regular tessellation patterns and fewer triangle faces compared to existing methods.
  arXiv  Detail & Related papers  (2022-10-05T16:52:25Z)
• A DeepParticle method for learning and generating aggregation patterns in multi-dimensional Keller-Segel chemotaxis systems [3.6184545598911724]
  We study a regularized interacting particle method for computing aggregation patterns and near singular solutions of a Keller-Segal (KS) chemotaxis system in two and three space dimensions. We further develop DeepParticle (DP) method to learn and generate solutions under variations of physical parameters.
  arXiv  Detail & Related papers  (2022-08-31T20:52:01Z)
• Machine Learning model for gas-liquid interface reconstruction in CFD numerical simulations [59.84561168501493]
  The volume of fluid (VoF) method is widely used in multi-phase flow simulations to track and locate the interface between two immiscible fluids. A major bottleneck of the VoF method is the interface reconstruction step due to its high computational cost and low accuracy on unstructured grids. We propose a machine learning enhanced VoF method based on Graph Neural Networks (GNN) to accelerate the interface reconstruction on general unstructured meshes.
  arXiv  Detail & Related papers  (2022-07-12T17:07:46Z)
• Consistent and symmetry preserving data-driven interface reconstruction for the level-set method [0.0]
  We focus on interface reconstruction (IR) in the level-set method, i.e. the computation of the volume fraction and apertures. The proposed approach improves accuracy for coarsely resolved interfaces and recovers the conventional IR for high resolutions. We provide details of floating point symmetric implementation and computational efficiency.
  arXiv  Detail & Related papers  (2021-04-23T13:21:10Z)
• Manifold Regularized Dynamic Network Pruning [102.24146031250034]
  This paper proposes a new paradigm that dynamically removes redundant filters by embedding the manifold information of all instances into the space of pruned networks. The effectiveness of the proposed method is verified on several benchmarks, which shows better performance in terms of both accuracy and computational cost.
  arXiv  Detail & Related papers  (2021-03-10T03:59:03Z)
• Primal-Dual Mesh Convolutional Neural Networks [62.165239866312334]
  We propose a primal-dual framework drawn from the graph-neural-network literature to triangle meshes. Our method takes features for both edges and faces of a 3D mesh as input and dynamically aggregates them. We provide theoretical insights of our approach using tools from the mesh-simplification literature.
  arXiv  Detail & Related papers  (2020-10-23T14:49:02Z)
• NPLIC: A Machine Learning Approach to Piecewise Linear Interface Construction [0.0]
  We propose an alternative neural network based method called NPLIC to perform PLIC calculations. We show that this data-driven approach results in accurate calculations at a fraction of the usual computational cost.
  arXiv  Detail & Related papers  (2020-06-26T16:48:53Z)
• Neural Subdivision [58.97214948753937]
  This paper introduces Neural Subdivision, a novel framework for data-driven coarseto-fine geometry modeling. We optimize for the same set of network weights across all local mesh patches, thus providing an architecture that is not constrained to a specific input mesh, fixed genus, or category. We demonstrate that even when trained on a single high-resolution mesh our method generates reasonable subdivisions for novel shapes.
  arXiv  Detail & Related papers  (2020-05-04T20:03:21Z)

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.