Many scientific phenomena are best understood as outcomes of underlying optimization problems, according to a new paper on arXiv. The authors of 'OptScientist: Automated Discovery of Optimization Laws' argue that automated scientific discovery has largely missed this perspective, instead focusing on recovering equations that describe observed input-output patterns.
OptScientist is presented as a step toward filling that gap: rather than fitting a function to data, it aims to discover the optimization laws that govern a system. The distinction is significant because an optimization law offers a different kind of explanation — one that accounts for why a system settles into the behaviour it does.
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