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Physikalische Gesellschaft Zürich
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Machine learning (ML) is having a profound impact on science by accelerating discoveries across physics, chemistry, and biology. Yet, entirely replacing our existing scientific workflows with purely data-driven ML models often yields unsatisfactory results. Such models struggle to generalise, require large amounts of training data, and are often seen as “black-boxes”. A more powerful approach may be to combine ML with our prior understanding of physics. Such physics-informed ML models can learn incrementally, generalise to new tasks and be more interpretable. In this talk we give an overview of the different ways physical principles can be combined with machine learning, and the impact this is having on scientific research.
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Physikalische Gesellschaft Zürich E-Mail: pgz_info at pgz.ch Web: http://www.pgz.ch |
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Zurich local time 4.5.2024 12:54:33 |
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