Data-driven techniques in rheology: Developments, Challenges and Perspective

D Mangal, A Jha, D Dabiri, S Jamali - Current Opinion in Colloid & Interface …, 2024 - Elsevier
With the rapid development and adoption of different data-driven techniques in rheology,
this review aims to reflect on the advent and growth of these frameworks, survey the state-of …

Autoencoders for discovering manifold dimension and coordinates in data from complex dynamical systems

K Zeng, CEP De Jesús, AJ Fox… - … Learning: Science and …, 2024 - iopscience.iop.org
While many phenomena in physics and engineering are formally high-dimensional, their
long-time dynamics often live on a lower-dimensional manifold. The present work introduces …

Data-driven Koopman operator predictions of turbulent dynamics in models of shear flows

CR Constante-Amores, AJ Fox, CEP De Jesús… - arXiv preprint arXiv …, 2024 - arxiv.org
The Koopman operator enables the analysis of nonlinear dynamical systems through a
linear perspective by describing time evolution in the infinite-dimensional space of …

[HTML][HTML] Rheo-SINDy: Finding a constitutive model from rheological data for complex fluids using sparse identification for nonlinear dynamics

T Sato, S Miyamoto, S Kato - Journal of Rheology, 2025 - pubs.aip.org
Rheology plays a pivotal role in understanding the flow behavior of fluids by discovering
governing equations that relate deformation and stress, known as constitutive equations …

Application of physics encoded neural networks to improve predictability of properties of complex multi-scale systems

MBJ Meinders, J Yang, E Linden - Scientific Reports, 2024 - nature.com
Predicting physical properties of complex multi-scale systems is a common challenge and
demands analysis of various temporal and spatial scales. However, physics alone is often …

Data-driven methods in Rheology

KH Ahn, S Jamali - Rheologica Acta, 2023 - Springer
With a consistent growth in computational power, even today's personal computers enable
storage and process of large amounts of data far beyond what was possible a decade ago …

Data-driven constitutive meta-modeling of nonlinear rheology via multifidelity neural networks

M Saadat, WH Hartt V, NJ Wagner, S Jamali - Journal of Rheology, 2024 - pubs.aip.org
Predicting the response of complex fluids to different flow conditions has been the focal point
of rheology and is generally done via constitutive relations. There are, nonetheless …

Recent developments on multiscale simulations for rheology and complex flow of polymers

T Sato, K Yoshimoto - Korea-Australia Rheology Journal, 2024 - Springer
This review summarized the multiscale simulation (MSS) methods for polymeric liquids.
Since polymeric liquids have multiscale characteristics of monomeric, mesoscopic, and …

Dynamics of a data-driven low-dimensional model of turbulent minimal pipe flow

CR Constante-Amores, AJ Linot… - arXiv preprint arXiv …, 2024 - arxiv.org
The simulation of turbulent flow requires many degrees of freedom to resolve all the relevant
times and length scales. However, due to the dissipative nature of the Navier-Stokes …

Strengthening our grip on food security by encoding physics into AI

MBJ Meinders, J Yang, E van der Linden - arXiv preprint arXiv:2311.09035, 2023 - arxiv.org
Climate change will jeopardize food security. Food security involves the robustness of the
global agri-food system. This agri-food system is intricately connected to systems centering …