Fractional rheology-informed neural networks for data-driven identification of viscoelastic constitutive models

Physics-informed neural network for inverse modeling of natural-state geothermal systems

(2023) Kazuya Ishitsuka et al.   APPLIED ENERGY

Physics-informed recurrent neural networks and hyper-parameter optimization for dynamic process systems

(2023) Tuse Asrav et al.   COMPUTERS & CHEMICAL ENGINEERING

The hidden hierarchical nature of soft particulate gels

(2023) Minaspi Bantawa et al.   Nature Physics

Physics-informed neural networks (PINNs) for fluid mechanics: a review

(2022) Shengze Cai et al.   ACTA MECHANICA SINICA

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

(2022) Mohammadamin Mahmoudabadbozchelou et al.   PROCEEDINGS OF THE NATIONAL ACADEMY OF SCIENCES OF THE UNITED STATES OF AMERICA

Data-driven selection of constitutive models via rheology-informed neural networks (RhINNs)

(2022) Milad Saadat et al.   RHEOLOGICA ACTA

Physics-informed neural networks for inverse problems in supersonic flows

(2022) Ameya D. Jagtap et al.   JOURNAL OF COMPUTATIONAL PHYSICS

Physics-informed neural networks combined with polynomial interpolation to solve nonlinear partial differential equations

(2022) Siping Tang et al.   COMPUTERS & MATHEMATICS WITH APPLICATIONS

Physics-Informed Neural Network (PINN) Evolution and Beyond: A Systematic Literature Review and Bibliometric Analysis

(2022) Zaharaddeen Karami Lawal et al.   Big Data and Cognitive Computing

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

(2021) Mohammadamin Mahmoudabadbozchelou et al.   JOURNAL OF RHEOLOGY

Rheology-Informed Neural Networks (RhINNs) for forward and inverse metamodelling of complex fluids

(2021) Mohammadamin Mahmoudabadbozchelou et al.   Scientific Reports

Learning unknown physics of non-Newtonian fluids

(2021) Brandon Reyes et al.   Physical Review Fluids

nn-PINNs: Non-Newtonian physics-informed neural networks for complex fluid modeling

(2021) Mohammadamin Mahmoudabadbozchelou et al.   Soft Matter

Hidden fluid mechanics: Learning velocity and pressure fields from flow visualizations

(2020) Maziar Raissi et al.   SCIENCE

On overall behavior of Maxwell mechanical model by the combined Caputo fractional derivative

(2020) Yi-Ying Feng et al.   CHINESE JOURNAL OF PHYSICS

Physics-informed neural networks for inverse problems in nano-optics and metamaterials

(2020) Yuyao Chen et al.   OPTICS EXPRESS

Application of fractional calculus methods to viscoelastic behaviours of solid propellants

(2020) Changqing Fang et al.   PHILOSOPHICAL TRANSACTIONS OF THE ROYAL SOCIETY A-MATHEMATICAL PHYSICAL AND ENGINEERING SCIENCES

On fitting data for parameter estimates: residual weighting and data representation

(2019) Piyush K. Singh et al.   RHEOLOGICA ACTA

Evaluation of Fractional Integrals and Derivatives of Elementary Functions: Overview and Tutorial

(2019) Roberto Garrappa et al.   Mathematics

Fractional derivative models for viscoelastic materials at finite deformations

(2019) Li-Jun Shen   INTERNATIONAL JOURNAL OF SOLIDS AND STRUCTURES

Comparative Study of Riemann–Liouville and Caputo Derivative Definitions in Time-Domain Analysis of Fractional-Order Capacitor

(2019) Yanwei Jiang et al.   IEEE TRANSACTIONS ON CIRCUITS AND SYSTEMS II-EXPRESS BRIEFS

The finite element implementation of 3D fractional viscoelastic constitutive models

(2018) Gioacchino Alotta et al.   FINITE ELEMENTS IN ANALYSIS AND DESIGN

Physics-informed neural networks: A deep learning framework for solving forward and inverse problems involving nonlinear partial differential equations

(2018) M. Raissi et al.   JOURNAL OF COMPUTATIONAL PHYSICS

Describing the firmness, springiness and rubberiness of food gels using fractional calculus. Part II: Measurements on semi-hard cheese

(2017) T.J. Faber et al.   FOOD HYDROCOLLOIDS

Quantifying the consistency and rheology of liquid foods using fractional calculus

(2017) Caroline E. Wagner et al.   FOOD HYDROCOLLOIDS

An adaptive parallel tempering method for the dynamic data-driven parameter estimation of nonlinear models

(2016) Matthew J. Armstrong et al.   AICHE JOURNAL

Quantitative rheological model selection: Good fits versus credible models using Bayesian inference

(2015) Jonathan B. Freund et al.   JOURNAL OF RHEOLOGY

A fractional K-BKZ constitutive formulation for describing the nonlinear rheology of multiscale complex fluids

(2014) Aditya Jaishankar et al.   JOURNAL OF RHEOLOGY

Power-law rheology in the bulk and at the interface: quasi-properties and fractional constitutive equations

(2012) A. Jaishankar et al.   PROCEEDINGS OF THE ROYAL SOCIETY A-MATHEMATICAL PHYSICAL AND ENGINEERING SCIENCES

On Riemann-Liouville and Caputo Derivatives

(2011) Changpin Li et al.   DISCRETE DYNAMICS IN NATURE AND SOCIETY

Peristaltic flow of viscoelastic fluid with fractional Maxwell model through a channel

(2009) D. Tripathi et al.   APPLIED MATHEMATICS AND COMPUTATION