Abstract
The project aims to research data-efficient and scalable grey-box modelling for end-point prediction of unit operations in pharmaceutical production processes. We will research a method that makes use of physics-informed neural networks and few-shot learning to achieve this. To enable broader applicability, we will investigate how to efficiently design and calibrate the models in a real-world setting. This method will yield a thorough understanding of the process state during each individual unit operation and provide a twofold benefit for Janssen Pharmaceutica: (1) increase efficiency and decrease cycle times of commercialized processes, and (2) deliver the Best Process At Launch (BPAL) for New Product Introductions (NPIs).
Researcher(s)
Research team(s)
Project type(s)