Learning Unknown Physics of non-Newtonian Fluids
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Abstract (arXiv)
We extend the physics-informed neural network (PINN) method to learn viscosity models of two non-Newtonian systems (polymer melts and suspensions of particles) using only velocity measurements. The PINN-inferred viscosity models agree with the empirical models for shear rates with large absolute values but deviate for shear rates near zero where the analytical models have an unphysical singularity. Once a viscosity model is learned, we use the PINN method to solve the momentum conservation equation for non-Newtonian fluid flow using only the boundary conditions.
Key facts. Cited by 13 papers in the map (data through 14 Oct 2026). First posted on arXiv 26 Aug 2020.
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Citing papers per year: 2021: 2; 2022: 4; 2023: 2; 2024: 3; 2025: 1; 2026: 1.
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- 2024From PINNs to PIKANs: Recent Advances in Physics-Informed Machine Learningcited by 73
- 2022DINER: Disorder-Invariant Implicit Neural Representationcited by 30
- 2023Disorder-invariant Implicit Neural Representationcited by 27
- 2021Physics-informed neural network simulation of multiphase poroelasticity using stress-split sequential trainingcited by 26
- 2022Constitutive model characterization and discovery using physics-informed deep learningcited by 17
- 2024Residual-based Attention Physics-informed Neural Networks for Spatio-Temporal Ageing Assessment of Transformers Operated in Renewable Power Plantscited by 11
- 2021Physics-constrained deep neural network method for estimating parameters in a redox flow batterycited by 9
- 2022Physics-informed neural network solution of thermo-hydro-mechanical (THM) processes in porous mediacited by 8
- 2023A Generative Modeling Framework for Inferring Families of Biomechanical Constitutive Laws in Data-Sparse Regimescited by 8
- 2022Physics-informed neural networks for non-Newtonian fluid thermo-mechanical problems: an application to rubber calendering processcited by 6
- 2024A Tutorial on the Use of Physics-Informed Neural Networks to Compute the Spectrum of Quantum Systemscited by 2
- 2025Training Deep Physics-Informed Kolmogorov-Arnold Networkscited by 2
- 2026An adaptive framework for the axisymmetric pulsar magnetosphere using physics-informed Kolmogorov-Arnold networks9 Jun 2026
- 2025Training Deep Physics-Informed Kolmogorov-Arnold Networks27 Oct 2025
- 2024From PINNs to PIKANs: Recent Advances in Physics-Informed Machine Learning17 Oct 2024
- 2024A Tutorial on the Use of Physics-Informed Neural Networks to Compute the Spectrum of Quantum Systems30 Jul 2024
- 2024Residual-based Attention Physics-informed Neural Networks for Spatio-Temporal Ageing Assessment of Transformers Operated in Renewable Power Plants10 May 2024
- 2023A Generative Modeling Framework for Inferring Families of Biomechanical Constitutive Laws in Data-Sparse Regimes4 May 2023
- 2023Disorder-invariant Implicit Neural Representation3 Apr 2023
- 2022DINER: Disorder-Invariant Implicit Neural Representation15 Nov 2022
- 2022Constitutive model characterization and discovery using physics-informed deep learning18 Mar 2022
- 2022Physics-informed neural network solution of thermo-hydro-mechanical (THM) processes in porous media3 Mar 2022
- 2022Physics-informed neural networks for non-Newtonian fluid thermo-mechanical problems: an application to rubber calendering process31 Jan 2022
- 2021Physics-informed neural network simulation of multiphase poroelasticity using stress-split sequential training6 Oct 2021
- 2024From PINNs to PIKANs: Recent Advances in Physics-Informed Machine Learningcited by 73
- 2022DINER: Disorder-Invariant Implicit Neural Representationcited by 30
- 2023Disorder-invariant Implicit Neural Representationcited by 27
- 2021Physics-informed neural network simulation of multiphase poroelasticity using stress-split sequential trainingcited by 26
- 2022Constitutive model characterization and discovery using physics-informed deep learningcited by 17
- 2024Residual-based Attention Physics-informed Neural Networks for Spatio-Temporal Ageing Assessment of Transformers Operated in Renewable Power Plantscited by 11
- 2021Physics-constrained deep neural network method for estimating parameters in a redox flow batterycited by 9
- 2022Physics-informed neural network solution of thermo-hydro-mechanical (THM) processes in porous mediacited by 8
- 2023A Generative Modeling Framework for Inferring Families of Biomechanical Constitutive Laws in Data-Sparse Regimescited by 8
- 2022Physics-informed neural networks for non-Newtonian fluid thermo-mechanical problems: an application to rubber calendering processcited by 6
- 2024A Tutorial on the Use of Physics-Informed Neural Networks to Compute the Spectrum of Quantum Systemscited by 2
- 2025Training Deep Physics-Informed Kolmogorov-Arnold Networkscited by 2
12 most cited of 13 All 13 (JSON)
- 2026An adaptive framework for the axisymmetric pulsar magnetosphere using physics-informed Kolmogorov-Arnold networks9 Jun 2026
- 2025Training Deep Physics-Informed Kolmogorov-Arnold Networks27 Oct 2025
- 2024From PINNs to PIKANs: Recent Advances in Physics-Informed Machine Learning17 Oct 2024
- 2024A Tutorial on the Use of Physics-Informed Neural Networks to Compute the Spectrum of Quantum Systems30 Jul 2024
- 2024Residual-based Attention Physics-informed Neural Networks for Spatio-Temporal Ageing Assessment of Transformers Operated in Renewable Power Plants10 May 2024
- 2023A Generative Modeling Framework for Inferring Families of Biomechanical Constitutive Laws in Data-Sparse Regimes4 May 2023
- 2023Disorder-invariant Implicit Neural Representation3 Apr 2023
- 2022DINER: Disorder-Invariant Implicit Neural Representation15 Nov 2022
- 2022Constitutive model characterization and discovery using physics-informed deep learning18 Mar 2022
- 2022Physics-informed neural network solution of thermo-hydro-mechanical (THM) processes in porous media3 Mar 2022
- 2022Physics-informed neural networks for non-Newtonian fluid thermo-mechanical problems: an application to rubber calendering process31 Jan 2022
- 2021Physics-informed neural network simulation of multiphase poroelasticity using stress-split sequential training6 Oct 2021
12 newest of 13 All 13 (JSON)