Reducing machine learning model emissions by integrating physics knowledge

          @misc{ scivideos_oai:cds.cern.ch:3025558,
            doi = {},
            url = {https://videos.cern.ch/record/3025558},
            author = {},
            keywords = {},
            language = {en},
            title = {Reducing machine learning model emissions by integrating physics knowledge},
            publisher = {},
            year = {2026},
            month = {may},
            note = {oai:cds.cern.ch:3025558 see, \url{https://scivideos.org/cern-cds/3025558}}
          }
          

Abstract

In many science and engineering disciplines, limited representative training data, poor reproducibility and low interpretability hinder the complete integration of AI. The environmental impact of this growing technology is also of particular concern. Physics-informed machine learning (PIML) seeks to answer the aforementioned challenges, with many techniques leveraging physical knowledge to reduce training data requirements. We aim to explore how and when these approaches reduce model emissions. In this initial work, we embed physical insight into our ML models, assessing performance and emissions jointly on two simple benchmarks, one synthetic and one from an engineering lab experiment. The work demonstrates how a PIML approach can reduce emissions based on reduced training data, yet also highlights the increased model complexity from additional hyperparameters to be optimised, and the tradeoff between these factors to decrease overall emissions.

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