Physics-Informed Neural Networks for Enhanced Interface Preservation in Lattice Boltzmann Multiphase Simulations

• URL: http://arxiv.org/abs/2504.10539v2
• Date: Fri, 18 Apr 2025 20:52:58 GMT
• Title: Physics-Informed Neural Networks for Enhanced Interface Preservation in Lattice Boltzmann Multiphase Simulations
• Authors: Yue Li, Lihong Zhang,
• Abstract summary: This paper presents an improved approach for preserving sharp interfaces in multiphase Lattice Boltzmann Method (LBM) simulations using Physics-Informed Neural Networks (PINNs)<n> Interface diffusion is a common challenge in multiphase LBM, leading to reduced accuracy in simulating phenomena where interfacial dynamics are critical.<n>We propose a coupled PINN-LBM framework that maintains interface sharpness while preserving the physical accuracy of the simulation.
• Score: 4.744635045603924
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: This paper presents an improved approach for preserving sharp interfaces in multiphase Lattice Boltzmann Method (LBM) simulations using Physics-Informed Neural Networks (PINNs). Interface diffusion is a common challenge in multiphase LBM, leading to reduced accuracy in simulating phenomena where interfacial dynamics are critical. We propose a coupled PINN-LBM framework that maintains interface sharpness while preserving the physical accuracy of the simulation. Our approach is validated through droplet simulations, with quantitative metrics measuring interface width, maximum gradient, phase separation, effective interface width, and interface energy. The enhanced visualization techniques employed in this work clearly demonstrate the superior performance of PINN-LBM over standard LBM for multiphase simulations, particularly in maintaining well-defined interfaces throughout the simulation. We provide a comprehensive analysis of the results, showcasing how the neural network integration effectively counteracts numerical diffusion, while maintaining physical consistency with the underlying fluid dynamics.

Related papers

• Multi-resolution Physics-Aware Recurrent Convolutional Neural Network for Complex Flows [2.7233737247962786]
  MRPARCv2 is designed to model complex flows by embedding the structure of advection-diffusion-reaction equations.<n>We evaluate the model on a challenging 2D turbulent radiative layer dataset from The Well multi-physics benchmark repository.
  arXiv  Detail & Related papers  (2025-12-04T16:19:10Z)
• Cross-Field Interface-Aware Neural Operators for Multiphase Flow Simulation [10.347532590928685]
  Interface Information-Aware Neural Operator (IANO) is a novel framework that explicitly leverages interface information as a physical prior to enhance the prediction accuracy.<n>IANO outperforms baselines by $sim$10% in accuracy for multiphase flow simulations while maintaining robustness under data-scarce and noise-perturbed conditions.
  arXiv  Detail & Related papers  (2025-11-09T01:13:40Z)
• Global Mean-Amplitude Enhanced Spiking Neural Network Coherent Ising Machine [6.142643738867997]
  A new global mean-amplitude feedback-enhanced spiking neural network CIM (GFSNN-CIM) is introduced.<n>The GFSNN-CIM achieves up to a 27% improvement in solution success rates compared to conventional spiking neural network CIM.
  arXiv  Detail & Related papers  (2025-09-17T11:23:24Z)
• Hybrid Neural-MPM for Interactive Fluid Simulations in Real-Time [57.30651532625017]
  We present a novel hybrid method that integrates numerical simulation, neural physics, and generative control.<n>Our system demonstrates robust performance across diverse 2D/3D scenarios, material types, and obstacle interactions.<n>We promise to release both models and data upon acceptance.
  arXiv  Detail & Related papers  (2025-05-25T01:27:18Z)
• Optimal Lattice Boltzmann Closures through Multi-Agent Reinforcement Learning [2.52656394551851]
  We present a novel, data-driven, multiagent learning (MARL) approach that drastically improves stability and accuracy of coarse-grained LBM simulations. We find that the MARL closures stabilize the simulations and recover the energy spectra of significantly more expensive fully resolved simulations.
  arXiv  Detail & Related papers  (2025-04-19T23:31:29Z)
• MIXPINN: Mixed-Material Simulations by Physics-Informed Neural Network [1.275845610262865]
  Traditional Finite Element Method (FEM)-based simulations are computationally expensive and impractical for real-time scenarios.<n>We introduce MIXPINN, a physics-informed Graph Neural Network (GNN) framework for mixed-material simulations.<n>By leveraging a graph-based representation of biomechanical structures, MIXPINN learns high-fidelity deformations from FEM-generated data and achieves real-time inference with sub-millimeter accuracy.
  arXiv  Detail & Related papers  (2025-03-17T12:48:29Z)
• HyperFLINT: Hypernetwork-based Flow Estimation and Temporal Interpolation for Scientific Ensemble Visualization [26.472939569860607]
  HyperFLINT is a novel deep learning-based approach for estimating flow fields, temporally interpolating fields, and facilitating parameter space exploration in ensemble data.<n>A series of experiments demonstrates HyperFLINT's significantly improved performance in flow field estimation, as well as its potential in enabling parameter space exploration.
  arXiv  Detail & Related papers  (2024-12-05T12:01:20Z)
• A Multi-Grained Symmetric Differential Equation Model for Learning Protein-Ligand Binding Dynamics [73.35846234413611]
  In drug discovery, molecular dynamics (MD) simulation provides a powerful tool for predicting binding affinities, estimating transport properties, and exploring pocket sites. We propose NeuralMD, the first machine learning (ML) surrogate that can facilitate numerical MD and provide accurate simulations in protein-ligand binding dynamics. We demonstrate the efficiency and effectiveness of NeuralMD, achieving over 1K$times$ speedup compared to standard numerical MD simulations.
  arXiv  Detail & Related papers  (2024-01-26T09:35:17Z)
• Near-realtime Facial Animation by Deep 3D Simulation Super-Resolution [7.14576106770047]
  We present a neural network-based simulation framework that can efficiently and realistically enhance a facial performance produced by a low-cost, realtime physics-based simulation. We use face animation as an exemplar of such a simulation domain, where creating this semantic congruence is achieved by simply dialing in the same muscle actuation controls and skeletal pose in the two simulators. Our proposed neural network super-resolution framework generalizes from this training set to unseen expressions, compensates for modeling discrepancies between the two simulations due to limited resolution or cost-cutting approximations in the real-time variant, and does not require any semantic descriptors or parameters to
  arXiv  Detail & Related papers  (2023-05-05T00:09:24Z)
• Hybrid quantum physics-informed neural networks for simulating computational fluid dynamics in complex shapes [37.69303106863453]
  We present a hybrid quantum physics-informed neural network that simulates laminar fluid flows in 3D Y-shaped mixers. Our approach combines the expressive power of a quantum model with the flexibility of a physics-informed neural network, resulting in a 21% higher accuracy compared to a purely classical neural network.
  arXiv  Detail & Related papers  (2023-04-21T20:49:29Z)
• 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)
• Multi-fidelity Hierarchical Neural Processes [79.0284780825048]
  Multi-fidelity surrogate modeling reduces the computational cost by fusing different simulation outputs. We propose Multi-fidelity Hierarchical Neural Processes (MF-HNP), a unified neural latent variable model for multi-fidelity surrogate modeling. We evaluate MF-HNP on epidemiology and climate modeling tasks, achieving competitive performance in terms of accuracy and uncertainty estimation.
  arXiv  Detail & Related papers  (2022-06-10T04:54:13Z)
• Collaborative Intelligent Reflecting Surface Networks with Multi-Agent Reinforcement Learning [63.83425382922157]
  Intelligent reflecting surface (IRS) is envisioned to be widely applied in future wireless networks. In this paper, we investigate a multi-user communication system assisted by cooperative IRS devices with the capability of energy harvesting.
  arXiv  Detail & Related papers  (2022-03-26T20:37:14Z)
• Influence Estimation and Maximization via Neural Mean-Field Dynamics [60.91291234832546]
  We propose a novel learning framework using neural mean-field (NMF) dynamics for inference and estimation problems. Our framework can simultaneously learn the structure of the diffusion network and the evolution of node infection probabilities.
  arXiv  Detail & Related papers  (2021-06-03T00:02:05Z)
• Combining Differentiable PDE Solvers and Graph Neural Networks for Fluid Flow Prediction [79.81193813215872]
  We develop a hybrid (graph) neural network that combines a traditional graph convolutional network with an embedded differentiable fluid dynamics simulator inside the network itself. We show that we can both generalize well to new situations and benefit from the substantial speedup of neural network CFD predictions.
  arXiv  Detail & Related papers  (2020-07-08T21:23:19Z)

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.