Development of a Fast-Solving Machine Learning Surrogate Model for a Pharmaceutical Manufacturing Digital Twin

<p>High-fidelity models (HFMs) for twin-screw wet granulation (TSWG) are often too computationally expensiv//e for routine calibration, optimization, and digital twin deployment. This chapter presents a faster, cheaper, and easier-to-use surrogate modeling workflow that preserves HFM-level predictiv...

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Autores principales:	Zandi, Mohammad, Ntamo, Donald
Formato:	Capítulo de libro
Lenguaje:	inglés
Publicado:	IntechOpen 2026
Materias:	Physical Sciences, Engineering and Technology
Acceso en línea:	https://www.intechopen.com/chapters/1238847
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