Data-driven techniques in rheology: Developments, Challenges and Perspective
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 …
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 …
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 …
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 …
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 …
demands analysis of various temporal and spatial scales. However, physics alone is often …
Data-driven constitutive meta-modeling of nonlinear rheology via multifidelity neural networks
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 …
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 …
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 …
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 …
global agri-food system. This agri-food system is intricately connected to systems centering …