“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-the-art methods relevant to rheological applications, and explore potential future directions. We classify different machine learning (ML) methodologies into data-centric and physics-informed frameworks.”

Find the paper and full list of authors in Current Opinion in Colloid & Interface Science.