首页|Reports from University of Minnesota Advance Knowledge in Machine Learning (Machine Learning In Process Systems Engineering: Challenges and Opportunities)
Reports from University of Minnesota Advance Knowledge in Machine Learning (Machine Learning In Process Systems Engineering: Challenges and Opportunities)
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NETL
NSTL
Elsevier
Investigators publish new report on Machine Learning. According to news reporting originating in Minneapolis, Minnesota, by NewsRx journalists, research stated, “This ‘white paper’is a concise perspective of the potential of machine learning in the process systems engineering (PSE) domain, based on a session during FIPSE 5, held in Crete, Greece, June 27-29, 2022.” Financial support for this research came from National Science Foundation (NSF). The news reporters obtained a quote from the research from the University of Minnesota, “The session included two invited talks and three short contributed presentations followed by extensive discussions. This paper does not intend to provide a comprehensive review on the subject or a detailed exposition of the discussions; instead its aim is to distill the main points of the discussions and talks, and in doing so, highlight open problems and directions for future research.” According to the news reporters, the research concluded: “The general conclusion from the session was that machine learning can have a transformational impact on the PSE domain enabling new discoveries and innovations, but research is needed to develop domain-specific techniques for problems in molecular/material design, data analytics, optimization, and control.” This research has been peer-reviewed.
MinneapolisMinnesotaUnited StatesNorth and Central AmericaCyborgsEmerging TechnologiesEngineeringMachine LearningUniversity of Minnesota
NETL
NSTL
Elsevier
