Machine learning methods for accelerated generative equipment design for new medicines
Ralph, Thomas (2022) Machine learning methods for accelerated generative equipment design for new medicines. In: CMAC Annual Open Day 2022, 2022-05-16 - 2022-05-18.
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Abstract
Simulation of the virus inactivation process within coiled reactors can be solved using multi -physics software such as COMSOL, Ansys Fluent, etc. These simulations can take up to weeks to finish processing, creating a bottleneck when trying to discover new medicines. The goal of this project is to design a variety of neural networks using machine learning which will aim to decrease virus inactivation simulation time.
Persistent Identifier
https://doi.org/10.17868/strath.00081761-
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Item type: Conference or Workshop Item(Poster) ID code: 81761 Dates: DateEvent16 May 2022PublishedSubjects: Medicine > Therapeutics. Pharmacology Department: Faculty of Science > Strathclyde Institute of Pharmacy and Biomedical Sciences Depositing user: Pure Administrator Date deposited: 08 Aug 2022 13:49 Last modified: 11 Nov 2024 17:06 URI: https://strathprints.strath.ac.uk/id/eprint/81761
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