About the project
Objective
This project aims to create a real-time digital twin framework enhanced with physics-informed neural networks (PINNs) to guide the development of plant-based meat analogs. By integrating mechanistic modeling with AI, the framework will provide deeper insight into protein structuring and deliver accurate, efficient tools for predicting and optimizing texture.
Background
Food production is a major driver of climate change, biodiversity loss, and public health challenges. While the transition to plant-based diets offers clear sustainability benefits, consumer acceptance is limited by the difficulty of reproducing the texture of meat. Current development approaches rely on costly, time-consuming trial-and-error experimentation. This project pioneers a systematic, data- and physics-driven strategy to accelerate texture design, reduce development costs, and enable more sustainable, nutritious, and appealing plant-based foods.
About the Digital Futures Postdoc Fellow
Jingnan Zhang holds a PhD in Food and Nutrition Science. Her research integrates computational modeling and soft matter physics with food science, enabling a systems-oriented approach to connect digital technologies with sustainable and resilient innovations for the future of food.
Main supervisor
Francisco Javier Vilaplana Domingo, Professor, Department of Chemistry, KTH Royal Institute of Technology.
Co-supervisor
Anna Hanner, Associate professor, Department of Fibre and Polymer Technology, KTH Royal Institute of Technology.
