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 …
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 …
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 …
Short Review on Machine Learning-Based Multi-Scale Simulation in Rheology
S Miyamoto - Nihon Reoroji Gakkaishi, 2024 - jstage.jst.go.jp
We briefly review the machine-learning (ML) applications for rheological research,
particularly on the multi-scale simulation (MSS) techniques for complex fluid flows. For such …
particularly on the multi-scale simulation (MSS) techniques for complex fluid flows. For such …