A seamless multiscale operator neural network for inferring bubble dynamics

DOI10.1017/jfm.2021.866zbMath1495.76119OpenAlexW3208453486MaRDI QIDQ5160149

Zhen Li, Martin R. Maxey, Chensen Lin, George Em. Karniadakis

Publication date: 28 October 2021

Published in: Journal of Fluid Mechanics (Search for Journal in Brave)

Full work available at URL: https://doi.org/10.1017/jfm.2021.866

zbMATH Keywords

machine learningRayleigh-Plesset equationdissipative particle dynamics methodbubble growth dynamicscomposite deep neural network

Mathematics Subject Classification ID

Learning and adaptive systems in artificial intelligence (68T05) Particle methods and lattice-gas methods (76M28) Liquid-gas two-phase flows, bubbly flows (76T10) Basic methods in fluid mechanics (76M99)

Related Items (11)

MIONet: Learning Multiple-Input Operators via Tensor Product ⋮ A comprehensive and fair comparison of two neural operators (with practical extensions) based on FAIR data ⋮ Scientific machine learning through physics-informed neural networks: where we are and what's next ⋮ Multifidelity deep operator networks for data-driven and physics-informed problems ⋮ A hybrid deep neural operator/finite element method for ice-sheet modeling ⋮ Wavelet neural operator for solving parametric partial differential equations in computational mechanics problems ⋮ Interfacing finite elements with deep neural operators for fast multiscale modeling of mechanics problems ⋮ SVD perspectives for augmenting DeepONet flexibility and interpretability ⋮ Reliable extrapolation of deep neural operators informed by physics or sparse observations ⋮ The anisotropic graph neural network model with multiscale and nonlinear characteristic for turbulence simulation ⋮ Unnamed Item

Uses Software

• DeepONet

Cites Work

• Unnamed Item
• Multiscale universal interface: a concurrent framework for coupling heterogeneous solvers
• Triple-decker: Interfacing atomistic-mesoscopic-continuum flow regimes
• B-PINNs: Bayesian physics-informed neural networks for forward and inverse PDE problems with noisy data
• Multi-oscillations of a bubble in a compressible liquid near a rigid boundary
• Bubble dynamics in a compressible liquid. Part 1. First-order theory
• DeepXDE: A Deep Learning Library for Solving Differential Equations

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