Harnessing Machine Learning for Hybrid Constitutive Modelling of Viscoelastic Fluid Flows in Computational Rheology | Paper Citation Record

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This paper cites On the formulation of rheological equations of state.

Harnessing Machine Learning for Hybrid Constitutive Modelling of Viscoelastic Fluid Flows in Computational Rheology On the formulation of rheological equations of state

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This paper cites A simple constitutive equation for polymer fluids based on the concept of deformation-dependent tensorial mobility.

Harnessing Machine Learning for Hybrid Constitutive Modelling of Viscoelastic Fluid Flows in Computational Rheology A simple constitutive equation for polymer fluids based on the concept of deformation-dependent tensorial mobility

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This paper cites A new constitutive equation derived from network theory.

Harnessing Machine Learning for Hybrid Constitutive Modelling of Viscoelastic Fluid Flows in Computational Rheology A new constitutive equation derived from network theory

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This paper cites Generalized viscoelastic models: their fractional equa- tions with solutions.

Harnessing Machine Learning for Hybrid Constitutive Modelling of Viscoelastic Fluid Flows in Computational Rheology Generalized viscoelastic models: their fractional equa- tions with solutions

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This paper cites Numerical simulation of non-linear elastic flows with a general collocated finite-volume method.

Harnessing Machine Learning for Hybrid Constitutive Modelling of Viscoelastic Fluid Flows in Computational Rheology Numerical simulation of non-linear elastic flows with a general collocated finite-volume method

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This paper cites Stabilization of an open-source finite-volume solver for viscoelastic fluid flows.

Harnessing Machine Learning for Hybrid Constitutive Modelling of Viscoelastic Fluid Flows in Computational Rheology Stabilization of an open-source finite-volume solver for viscoelastic fluid flows

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Harnessing Machine Learning for Hybrid Constitutive Modelling of Viscoelastic Fluid Flows in Computational Rheology Unresolved cited work

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This paper cites Benchmark solutions for the flow of Oldroyd-B and PTT fluids in planar contractions.

Harnessing Machine Learning for Hybrid Constitutive Modelling of Viscoelastic Fluid Flows in Computational Rheology Benchmark solutions for the flow of Oldroyd-B and PTT fluids in planar contractions

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This paper cites nn-PINNs: Non-Newtonian physics-informed neural networks for complex fluid modeling.

Harnessing Machine Learning for Hybrid Constitutive Modelling of Viscoelastic Fluid Flows in Computational Rheology nn-PINNs: Non-Newtonian physics-informed neural networks for complex fluid modeling

Reference 9

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This paper cites RheologyNet: A physics-informed neural network solution to evaluate the thixotropic properties of cementitious materials.

Harnessing Machine Learning for Hybrid Constitutive Modelling of Viscoelastic Fluid Flows in Computational Rheology RheologyNet: A physics-informed neural network solution to evaluate the thixotropic properties of cementitious materials

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This paper cites ViscoelasticNet: A physics informed neural network framework for stress discovery and model selection.

Harnessing Machine Learning for Hybrid Constitutive Modelling of Viscoelastic Fluid Flows in Computational Rheology ViscoelasticNet: A physics informed neural network framework for stress discovery and model selection

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This paper cites Data-driven selection of constitutive models via rheology- informed neural networks (RhINNs).

Harnessing Machine Learning for Hybrid Constitutive Modelling of Viscoelastic Fluid Flows in Computational Rheology Data-driven selection of constitutive models via rheology- informed neural networks (RhINNs)

Reference 12

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This paper cites Data-driven constitutive model of complex fluids using recurrent neural networks.

Harnessing Machine Learning for Hybrid Constitutive Modelling of Viscoelastic Fluid Flows in Computational Rheology Data-driven constitutive model of complex fluids using recurrent neural networks

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This paper cites Recurrent neural networks (RNNs) learn the constitutive law of viscoelasticity.

Harnessing Machine Learning for Hybrid Constitutive Modelling of Viscoelastic Fluid Flows in Computational Rheology Recurrent neural networks (RNNs) learn the constitutive law of viscoelasticity

Reference 14

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This paper cites One test to predict them all: Rheological characterization of complex fluids via artificial neural network.

Harnessing Machine Learning for Hybrid Constitutive Modelling of Viscoelastic Fluid Flows in Computational Rheology One test to predict them all: Rheological characterization of complex fluids via artificial neural network

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This paper cites RheOFormer: A generative transformer model for simulation of complex fluids and flows.

Harnessing Machine Learning for Hybrid Constitutive Modelling of Viscoelastic Fluid Flows in Computational Rheology RheOFormer: A generative transformer model for simulation of complex fluids and flows

Reference 16

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This paper cites Rheo-SINDy: Finding a constitutive model from rheological data for complex fluids using sparse identification for nonlinear dynamics.

Harnessing Machine Learning for Hybrid Constitutive Modelling of Viscoelastic Fluid Flows in Computational Rheology Rheo-SINDy: Finding a constitutive model from rheological data for complex fluids using sparse identification for nonlinear dynamics

Reference 17

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This paper cites Sparse regression for discovery of constitutive models from oscillatory shear mea- surements.

Harnessing Machine Learning for Hybrid Constitutive Modelling of Viscoelastic Fluid Flows in Computational Rheology Sparse regression for discovery of constitutive models from oscillatory shear mea- surements

Reference 18

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This paper cites Hammering at the entropy: a GENERIC- guided approach to learning polymeric rheological constitutive equations using PINNs.

Harnessing Machine Learning for Hybrid Constitutive Modelling of Viscoelastic Fluid Flows in Computational Rheology Hammering at the entropy: a GENERIC- guided approach to learning polymeric rheological constitutive equations using PINNs

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This paper cites Cfd-Nn Coupling for the Simulation of Complex Fluids.

Harnessing Machine Learning for Hybrid Constitutive Modelling of Viscoelastic Fluid Flows in Computational Rheology Cfd-Nn Coupling for the Simulation of Complex Fluids

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This paper cites Scientific machine learning for modeling and simulating complex fluids.

Harnessing Machine Learning for Hybrid Constitutive Modelling of Viscoelastic Fluid Flows in Computational Rheology Scientific machine learning for modeling and simulating complex fluids

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This paper cites Learning constitutive models and rheology from partial flow measurements.

Harnessing Machine Learning for Hybrid Constitutive Modelling of Viscoelastic Fluid Flows in Computational Rheology Learning constitutive models and rheology from partial flow measurements

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This paper cites Unbiased construction of constitutive relations for soft materials from experiments via rheology-informed neural networks.

Harnessing Machine Learning for Hybrid Constitutive Modelling of Viscoelastic Fluid Flows in Computational Rheology Unbiased construction of constitutive relations for soft materials from experiments via rheology-informed neural networks

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This paper cites Learning a family of rheological constitutive models using neural operators.

Harnessing Machine Learning for Hybrid Constitutive Modelling of Viscoelastic Fluid Flows in Computational Rheology Learning a family of rheological constitutive models using neural operators

Reference 24

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This paper cites Data- driven physics-informed constitutive metamodeling of complex fluids: A multifidelity neural network (MFNN) framework.

Harnessing Machine Learning for Hybrid Constitutive Modelling of Viscoelastic Fluid Flows in Computational Rheology Data- driven physics-informed constitutive metamodeling of complex fluids: A multifidelity neural network (MFNN) framework

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This paper cites Machine learning for viscoelastic constitutive model identification and parameterisation using large amplitude oscillatory shear.

Harnessing Machine Learning for Hybrid Constitutive Modelling of Viscoelastic Fluid Flows in Computational Rheology Machine learning for viscoelastic constitutive model identification and parameterisation using large amplitude oscillatory shear

Reference 26

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This paper cites Digital rheometer twins: Learning the hidden rheology of complex fluids through rheology-informed graph neural networks.

Harnessing Machine Learning for Hybrid Constitutive Modelling of Viscoelastic Fluid Flows in Computational Rheology Digital rheometer twins: Learning the hidden rheology of complex fluids through rheology-informed graph neural networks

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This paper cites PINNsFormer: A Transformer-Based Framework For Physics-Informed Neural Networks.

Harnessing Machine Learning for Hybrid Constitutive Modelling of Viscoelastic Fluid Flows in Computational Rheology PINNsFormer: A Transformer-Based Framework For Physics-Informed Neural Networks

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This paper cites Attention is all you need.

Harnessing Machine Learning for Hybrid Constitutive Modelling of Viscoelastic Fluid Flows in Computational Rheology Attention is all you need

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This paper cites Fourier Neural Operator for Parametric Partial Differential Equations.

Harnessing Machine Learning for Hybrid Constitutive Modelling of Viscoelastic Fluid Flows in Computational Rheology Fourier Neural Operator for Parametric Partial Differential Equations

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This paper cites A composite neural network that learns from multi-fidelity data: Application to function approximation and inverse PDE problems.

Harnessing Machine Learning for Hybrid Constitutive Modelling of Viscoelastic Fluid Flows in Computational Rheology A composite neural network that learns from multi-fidelity data: Application to function approximation and inverse PDE problems

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This paper cites Multiscale simulations for viscoelastic fluids with approximate constitutive models derived by a sparse identification method.

Harnessing Machine Learning for Hybrid Constitutive Modelling of Viscoelastic Fluid Flows in Computational Rheology Multiscale simulations for viscoelastic fluids with approximate constitutive models derived by a sparse identification method

Reference 32

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This paper cites A survey on the application of machine learning in turbulent flow simulations.

Harnessing Machine Learning for Hybrid Constitutive Modelling of Viscoelastic Fluid Flows in Computational Rheology A survey on the application of machine learning in turbulent flow simulations

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This paper cites Reynolds averaged turbulence modelling using deep neural networks with embedded invariance.

Harnessing Machine Learning for Hybrid Constitutive Modelling of Viscoelastic Fluid Flows in Computational Rheology Reynolds averaged turbulence modelling using deep neural networks with embedded invariance

Reference 34

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This paper cites Finding the underlying viscoelastic constitutive equation via universal differential equations and differentiable physics.

Harnessing Machine Learning for Hybrid Constitutive Modelling of Viscoelastic Fluid Flows in Computational Rheology Finding the underlying viscoelastic constitutive equation via universal differential equations and differentiable physics

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This paper cites Discovering governing equations from data by sparse identification of nonlinear dynamical systems.

Harnessing Machine Learning for Hybrid Constitutive Modelling of Viscoelastic Fluid Flows in Computational Rheology Discovering governing equations from data by sparse identification of nonlinear dynamical systems

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This paper cites On the high Weissenberg number problem.

Harnessing Machine Learning for Hybrid Constitutive Modelling of Viscoelastic Fluid Flows in Computational Rheology On the high Weissenberg number problem

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This paper cites The log-conformation tensor approach in the finite-volume method framework.

Harnessing Machine Learning for Hybrid Constitutive Modelling of Viscoelastic Fluid Flows in Computational Rheology The log-conformation tensor approach in the finite-volume method framework

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Harnessing Machine Learning for Hybrid Constitutive Modelling of Viscoelastic Fluid Flows in Computational Rheology Numerical methods for viscoelastic fluid flows

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Harnessing Machine Learning for Hybrid Constitutive Modelling of Viscoelastic Fluid Flows in Computational Rheology Universal Differential Equations for Scientific Machine Learning

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Harnessing Machine Learning for Hybrid Constitutive Modelling of Viscoelastic Fluid Flows in Computational Rheology Neural ordinary differential equations

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Harnessing Machine Learning for Hybrid Constitutive Modelling of Viscoelastic Fluid Flows in Computational Rheology Multilayer feedforward networks are universal approximators

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This paper cites The theory of matrix polynomials and its application to the mechanics of isotropic continua.

Harnessing Machine Learning for Hybrid Constitutive Modelling of Viscoelastic Fluid Flows in Computational Rheology The theory of matrix polynomials and its application to the mechanics of isotropic continua

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Harnessing Machine Learning for Hybrid Constitutive Modelling of Viscoelastic Fluid Flows in Computational Rheology On isotropic functions of symmetric tensors, skew-symmetric tensors and vectors

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Harnessing Machine Learning for Hybrid Constitutive Modelling of Viscoelastic Fluid Flows in Computational Rheology Clarifying the representation of isotropic symmetric tensor-valued functions of two symmetric tensors

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Harnessing Machine Learning for Hybrid Constitutive Modelling of Viscoelastic Fluid Flows in Computational Rheology Enforcing Dirichlet boundary conditions in physics-informed neural networks and variational physics-informed neural networks

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This paper cites F.The theory of Polymer Dynamics.

Harnessing Machine Learning for Hybrid Constitutive Modelling of Viscoelastic Fluid Flows in Computational Rheology F.The theory of Polymer Dynamics

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Harnessing Machine Learning for Hybrid Constitutive Modelling of Viscoelastic Fluid Flows in Computational Rheology Pytorch: An imperative style, high-performance deep learning library

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Harnessing Machine Learning for Hybrid Constitutive Modelling of Viscoelastic Fluid Flows in Computational Rheology Compiling machine learning programs via high-level tracing

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Harnessing Machine Learning for Hybrid Constitutive Modelling of Viscoelastic Fluid Flows in Computational Rheology Unresolved cited work

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This paper cites A general approach for running Python codes in OpenFOAM using an embedded Pybind11 Python interpreter.

Harnessing Machine Learning for Hybrid Constitutive Modelling of Viscoelastic Fluid Flows in Computational Rheology A general approach for running Python codes in OpenFOAM using an embedded Pybind11 Python interpreter

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Harnessing Machine Learning for Hybrid Constitutive Modelling of Viscoelastic Fluid Flows in Computational Rheology Understanding the difficulty of training deep feedforward neural networks

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Harnessing Machine Learning for Hybrid Constitutive Modelling of Viscoelastic Fluid Flows in Computational Rheology Batch normalization: Accelerating deep network training by reducing internal covariate shift

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This paper cites A review of nonlinear oscillatory shear tests: Analysis and application of large amplitude oscillatory shear (LAOS).

Harnessing Machine Learning for Hybrid Constitutive Modelling of Viscoelastic Fluid Flows in Computational Rheology A review of nonlinear oscillatory shear tests: Analysis and application of large amplitude oscillatory shear (LAOS)

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This paper cites Self-consistent Fourier–Tschebyshev rep- resentations of the first normal stress difference in large amplitude oscillatory shear.

Harnessing Machine Learning for Hybrid Constitutive Modelling of Viscoelastic Fluid Flows in Computational Rheology Self-consistent Fourier–Tschebyshev rep- resentations of the first normal stress difference in large amplitude oscillatory shear

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Harnessing Machine Learning for Hybrid Constitutive Modelling of Viscoelastic Fluid Flows in Computational Rheology Unresolved cited work

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Harnessing Machine Learning for Hybrid Constitutive Modelling of Viscoelastic Fluid Flows in Computational Rheology Unresolved cited work

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Harnessing Machine Learning for Hybrid Constitutive Modelling of Viscoelastic Fluid Flows in Computational Rheology Adam: A Method for Stochastic Optimization

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Harnessing Machine Learning for Hybrid Constitutive Modelling of Viscoelastic Fluid Flows in Computational Rheology On the limited memory BFGS method for large scale optimization

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Harnessing Machine Learning for Hybrid Constitutive Modelling of Viscoelastic Fluid Flows in Computational Rheology An overview of overfitting and its solutions

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Harnessing Machine Learning for Hybrid Constitutive Modelling of Viscoelastic Fluid Flows in Computational Rheology A nonlinear network viscoelastic model

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Harnessing Machine Learning for Hybrid Constitutive Modelling of Viscoelastic Fluid Flows in Computational Rheology Working group on numerical techniques

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Harnessing Machine Learning for Hybrid Constitutive Modelling of Viscoelastic Fluid Flows in Computational Rheology Dynamics of high-Deborah-number entry flows: a numerical study

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Harnessing Machine Learning for Hybrid Constitutive Modelling of Viscoelastic Fluid Flows in Computational Rheology Numerical simulation of the planar contraction flow of a Giesekus fluid

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Harnessing Machine Learning for Hybrid Constitutive Modelling of Viscoelastic Fluid Flows in Computational Rheology Effect of the contraction ratio upon viscoelastic fluid flow in three-dimensional square–square contractions

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Harnessing Machine Learning for Hybrid Constitutive Modelling of Viscoelastic Fluid Flows in Computational Rheology Extrapolation limitations of multilayer feedforward neural networks

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Harnessing Machine Learning for Hybrid Constitutive Modelling of Viscoelastic Fluid Flows in Computational Rheology Large amplitude oscilla- tory extension (LAOE) of dilute polymer solutions

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Harnessing Machine Learning for Hybrid Constitutive Modelling of Viscoelastic Fluid Flows in Computational Rheology Optimized cross-slot flow geometry for microfluidic extensional rheometry

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Harnessing Machine Learning for Hybrid Constitutive Modelling of Viscoelastic Fluid Flows in Computational Rheology Purely elastic flow asymmetries

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Harnessing Machine Learning for Hybrid Constitutive Modelling of Viscoelastic Fluid Flows in Computational Rheology A new viscoelastic benchmark flow: Stationary bifurcation in a cross-slot

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Harnessing Machine Learning for Hybrid Constitutive Modelling of Viscoelastic Fluid Flows in Computational Rheology On extensibility effects in the cross-slot flow bifurcation

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