Fractional rheology-informed neural networks for data-driven identification of viscoelastic constitutive models
出版年份 2023 全文链接
标题
Fractional rheology-informed neural networks for data-driven identification of viscoelastic constitutive models
作者
关键词
-
出版物
RHEOLOGICA ACTA
Volume -, Issue -, Pages -
出版商
Springer Science and Business Media LLC
发表日期
2023-08-26
DOI
10.1007/s00397-023-01408-w
参考文献
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注意:仅列出部分参考文献,下载原文获取全部文献信息。- 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
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