Adding Low-Fidelity Data to ML Training Sets Could Yield Better Process Models

This article in Genetic Engineering & Biotechnology News covers our work within the ARC Digital Bioprocess Development Hub, where we are exploring how abundant low-fidelity data can help models learn broad process trends, while smaller amounts of high-fidelity data refine those trends and align predictions more closely with the available ground truth.