Data-driven selection of constitutive models via rheology-informed neural networks (RhINNs)
出版年份 2022 全文链接
标题
Data-driven selection of constitutive models via rheology-informed neural networks (RhINNs)
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
2022-08-03
DOI
10.1007/s00397-022-01357-w
参考文献
相关参考文献
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 networks (PINNs) for fluid mechanics: a review
- (2022) Shengze Cai et al. ACTA MECHANICA SINICA
- Scientific Machine Learning Through Physics–Informed Neural Networks: Where we are and What’s Next
- (2022) Salvatore Cuomo et al. JOURNAL OF SCIENTIFIC COMPUTING
- Machine learning for metal additive manufacturing: predicting temperature and melt pool fluid dynamics using physics-informed neural networks
- (2021) Qiming Zhu et al. COMPUTATIONAL MECHANICS
- Data-driven physics-informed constitutive metamodeling of complex fluids: A multifidelity neural network (MFNN) framework
- (2021) Mohammadamin Mahmoudabadbozchelou et al. JOURNAL OF RHEOLOGY
- Mechanics and structure of carbon black gels under high-power ultrasound
- (2021) Noémie Dagès et al. JOURNAL OF RHEOLOGY
- Digital twin, physics-based model, and machine learning applied to damage detection in structures
- (2021) T.G. Ritto et al. MECHANICAL SYSTEMS AND SIGNAL PROCESSING
- Rheology-Informed Neural Networks (RhINNs) for forward and inverse metamodelling of complex fluids
- (2021) Mohammadamin Mahmoudabadbozchelou et al. Scientific Reports
- Three ways to solve partial differential equations with neural networks — A review
- (2021) Jan Blechschmidt et al. GAMM Mitteilungen
- On an artificial neural network for inverse scattering problems
- (2021) Yu Gao et al. JOURNAL OF COMPUTATIONAL PHYSICS
- Multifidelity modeling for Physics-Informed Neural Networks (PINNs)
- (2021) Michael Penwarden et al. JOURNAL OF COMPUTATIONAL PHYSICS
- Thixotropy, nonmonotonic stress relaxation, and the second law of thermodynamics
- (2021) Yogesh M. Joshi JOURNAL OF RHEOLOGY
- nn-PINNs: Non-Newtonian physics-informed neural networks for complex fluid modeling
- (2021) Mohammadamin Mahmoudabadbozchelou et al. Soft Matter
- Variations of the Herschel–Bulkley exponent reflecting contributions of the viscous continuous phase to the shear rate-dependent stress of soft glassy materials
- (2020) Marco Caggioni et al. JOURNAL OF RHEOLOGY
- Hidden fluid mechanics: Learning velocity and pressure fields from flow visualizations
- (2020) Maziar Raissi et al. SCIENCE
- Study Rheological Behavior of Polymer Solution in Different-Medium-Injection-Tools
- (2019) Bin Huang et al. Polymers
- The viscosity-radius relationship for concentrated polymer solutions
- (2019) Dave E. Dunstan Scientific Reports
- Colloidal Gels with Tunable Mechanomorphology Regulate Endothelial Morphogenesis
- (2019) Smruti K. Nair et al. Scientific Reports
- A review of thixotropy and its rheological modeling
- (2019) Ronald G. Larson et al. JOURNAL OF RHEOLOGY
- Machine Learning for Fluid Mechanics
- (2019) Steven L. Brunton et al. Annual Review of Fluid Mechanics
- 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
- Deep Reinforcement Learning: A Brief Survey
- (2017) Kai Arulkumaran et al. IEEE SIGNAL PROCESSING MAGAZINE
- A New Empirical Model for Bulk Foam Rheology
- (2017) Aboozar Soleymanzadeh et al. JOURNAL OF ENERGY RESOURCES TECHNOLOGY-TRANSACTIONS OF THE ASME
- Prediction of rheology of shear thickening fluids using phenomenological and artificial neural network models
- (2017) Sanchi Arora et al. KOREA-AUSTRALIA RHEOLOGY JOURNAL
- Physics-informed machine learning approach for reconstructing Reynolds stress modeling discrepancies based on DNS data
- (2017) Jian-Xun Wang et al. Physical Review Fluids
- An adaptive parallel tempering method for the dynamic data-driven parameter estimation of nonlinear models
- (2016) Matthew J. Armstrong et al. AICHE JOURNAL
- Dynamic shear rheology of a thixotropic suspension: Comparison of an improved structure-based model with large amplitude oscillatory shear experiments
- (2016) Matthew J. Armstrong et al. JOURNAL OF RHEOLOGY
- Quantitative rheological model selection: Good fits versus credible models using Bayesian inference
- (2015) Jonathan B. Freund et al. JOURNAL OF RHEOLOGY
- Constitutive equations for thixotropic fluids
- (2015) R. G. Larson JOURNAL OF RHEOLOGY
- A comprehensive constitutive law for waxy crude oil: a thixotropic yield stress fluid
- (2014) Christopher J. Dimitriou et al. Soft Matter
- Comparative evaluation of support vector machines for computer aided diagnosis of lung cancer in CT based on a multi-dimensional data set
- (2013) Tao Sun et al. COMPUTER METHODS AND PROGRAMS IN BIOMEDICINE
- Microstructure and rheology of a flow-induced structured phase in wormlike micellar solutions
- (2013) J. J. Cardiel et al. PROCEEDINGS OF THE NATIONAL ACADEMY OF SCIENCES OF THE UNITED STATES OF AMERICA
- Thixotropic elasto-viscoplastic model for structured fluids
- (2011) Paulo R. de Souza Mendes Soft Matter
Create your own webinar
Interested in hosting your own webinar? Check the schedule and propose your idea to the Peeref Content Team.
Create NowBecome a Peeref-certified reviewer
The Peeref Institute provides free reviewer training that teaches the core competencies of the academic peer review process.
Get Started