Related references
Note: Only part of the references are listed.Minireview on Lattice Boltzmann Modeling of Gas Flow and Adsorption in Shale Porous Media: Progress and Future Direction
Jianlin Zhao et al.
ENERGY & FUELS (2023)
Computation of Effective Viscosities for Rarefied Gas Flows Using Ray-Tracing
Jean-Michel Tucny et al.
International Journal of Applied and Computational Mathematics (2023)
Data-driven constitutive relation reveals scaling law for hydrodynamic transport coefficients
Candi Zheng et al.
PHYSICAL REVIEW E (2023)
A Physics-Informed Neural Network for Quantifying the Microstructural Properties of Polycrystalline Nickel Using Ultrasound Data: A promising approach for solving inverse problems
Khemraj Shukla et al.
IEEE SIGNAL PROCESSING MAGAZINE (2022)
Deep Kronecker neural networks: A general framework for neural networks with adaptive activation functions
Ameya D. Jagtap et al.
NEUROCOMPUTING (2022)
Deep learning of inverse water waves problems using multi-fidelity data: Application to Serre-Green-Naghdi equations
Ameya D. Jagtap et al.
OCEAN ENGINEERING (2022)
Scientific Machine Learning Through Physics-Informed Neural Networks: Where we are and What's Next
Salvatore Cuomo et al.
JOURNAL OF SCIENTIFIC COMPUTING (2022)
A modified lattice Boltzmann model for microcylindrical Couette gas flows
Junjie Ren et al.
PHYSICA SCRIPTA (2022)
Physics-informed neural networks for inverse problems in supersonic flows
Ameya D. Jagtap et al.
JOURNAL OF COMPUTATIONAL PHYSICS (2022)
Physics-informed neural networks (PINNs) for fluid mechanics: a review
Shengze Cai et al.
ACTA MECHANICA SINICA (2021)
Physics-informed neural networks for solving forward and inverse flow problems via the Boltzmann-BGK formulation
Qin Lou et al.
JOURNAL OF COMPUTATIONAL PHYSICS (2021)
Parallel physics-informed neural networks via domain decomposition
Khemraj Shukla et al.
JOURNAL OF COMPUTATIONAL PHYSICS (2021)
hp-VPINNs: Variational physics-informed neural networks with domain decomposition
Ehsan Kharazmi et al.
COMPUTER METHODS IN APPLIED MECHANICS AND ENGINEERING (2021)
Learning unknown physics of non-Newtonian fluids
Brandon Reyes et al.
PHYSICAL REVIEW FLUIDS (2021)
UNDERSTANDING AND MITIGATING GRADIENT FLOW PATHOLOGIES IN PHYSICS-INFORMED NEURAL NETWORKS
Sifan Wang et al.
SIAM JOURNAL ON SCIENTIFIC COMPUTING (2021)
Physics-informed machine learning
George Em Karniadakis et al.
NATURE REVIEWS PHYSICS (2021)
DeepXDE: A Deep Learning Library for Solving Differential Equations
Lu Lu et al.
SIAM REVIEW (2021)
Adaptive activation functions accelerate convergence in deep and physics-informed neural networks
Ameya D. Jagtap et al.
JOURNAL OF COMPUTATIONAL PHYSICS (2020)
Physics-informed neural networks for high-speed flows
Zhiping Mao et al.
COMPUTER METHODS IN APPLIED MECHANICS AND ENGINEERING (2020)
Data-driven discovery of governing equations for fluid dynamics based on molecular simulation
Jun Zhang et al.
JOURNAL OF FLUID MECHANICS (2020)
Conservative physics-informed neural networks on discrete domains for conservation laws: Applications to forward and inverse problems
Ameya D. Jagtap et al.
COMPUTER METHODS IN APPLIED MECHANICS AND ENGINEERING (2020)
Nonlinear transport of rarefied Couette flows from low speed to high speed
Jihui Ou et al.
PHYSICS OF FLUIDS (2020)
Physics-informed neural networks: A deep learning framework for solving forward and inverse problems involving nonlinear partial differential equations
M. Raissi et al.
JOURNAL OF COMPUTATIONAL PHYSICS (2019)
Uniformly accurate machine learning-based hydrodynamic models for kinetic equations
Jiequn Han et al.
PROCEEDINGS OF THE NATIONAL ACADEMY OF SCIENCES OF THE UNITED STATES OF AMERICA (2019)
A comparative study of discrete velocity methods for low-speed rarefied gas flows
Peng Wang et al.
COMPUTERS & FLUIDS (2018)
Hidden physics models: Machine learning of nonlinear partial differential equations
Maziar Raissi et al.
JOURNAL OF COMPUTATIONAL PHYSICS (2018)
Numerical simulation of flows from free molecular regime to continuum regime by a DVM with streaming and collision processes
L. M. Yang et al.
JOURNAL OF COMPUTATIONAL PHYSICS (2016)
Theoretical description of the gaseous Knudsen layer in Couette flow based on the second-order constitutive and slip-jump models
R. S. Myong
PHYSICS OF FLUIDS (2016)
The planar Couette flow with slip and jump boundary conditions in a microchannel
Mohamed Hssikou et al.
MONTE CARLO METHODS AND APPLICATIONS (2016)
Molecular free paths in nanoscale gas flows
Murat Barisik et al.
MICROFLUIDICS AND NANOFLUIDICS (2015)
The effect of Knudsen layers on rarefied cylindrical Couette gas flows
Nishanth Dongari et al.
MICROFLUIDICS AND NANOFLUIDICS (2013)
Discrete unified gas kinetic scheme for all Knudsen number flows: Low-speed isothermal case
Zhaoli Guo et al.
PHYSICAL REVIEW E (2013)
Effects of curvature on rarefied gas flows between rotating concentric cylinders
Nishanth Dongari et al.
PHYSICS OF FLUIDS (2013)
Hydrodynamic Model for Conductivity in Graphene
M. Mendoza et al.
SCIENTIFIC REPORTS (2013)
Macroscopic transport models for rarefied gas flows: a brief review
Henning Struchtrup et al.
IMA JOURNAL OF APPLIED MATHEMATICS (2011)
Accuracy analysis of high-order lattice Boltzmann models for rarefied gas flows
Jianping Meng et al.
JOURNAL OF COMPUTATIONAL PHYSICS (2011)
Gauss-Hermite quadratures and accuracy of lattice Boltzmann models for nonequilibrium gas flows
Jianping Meng et al.
PHYSICAL REVIEW E (2011)
Analysis of lattice Boltzmann equation for microscale gas flows: Relaxation times, boundary conditions and the Knudsen layer
Zhaoli Guo et al.
INTERNATIONAL JOURNAL OF COMPUTATIONAL FLUID DYNAMICS (2008)
Accuracy of higher-order lattice Boltzmann methods for microscale flows with finite Knudsen numbers
Seung Hyun Kim et al.
JOURNAL OF COMPUTATIONAL PHYSICS (2008)
Lattice Boltzmann equation with multiple effective relaxation times for gaseous microscale flow
Zhaoli Guo et al.
PHYSICAL REVIEW E (2008)
An extended Navier-Stokes formulation for gas flows in the Knudsen layer near a wall
Z. L. Guo et al.
EPL (2007)
Kinetic theory representation of hydrodynamics: a way beyond the Navier-Stokes equation
XW Shan et al.
JOURNAL OF FLUID MECHANICS (2006)
Failures of the Burnett and super-Burnett equations in steady state processes
H Struchtrup
CONTINUUM MECHANICS AND THERMODYNAMICS (2005)
Regularization of Grad's 13 moment equations: Derivation and linear analysis
H Struchtrup et al.
PHYSICS OF FLUIDS (2003)