Statut | Confirmé |
Série | FORUM-ENS |
Domaines | cond-mat.stat-mech |
Date | Mercredi 20 Octobre 2021 |
Heure | 14:30 |
Institut | LPENS |
Salle | L367 |
Nom de l'orateur | Sethna |
Prenom de l'orateur | James |
Addresse email de l'orateur | |
Institution de l'orateur | Cornell University |
Titre | Sloppy models, differential geometry, and why science works |
Résumé | Models of systems biology, climate change, ecology, complex instruments, and macroeconomics have parameters that are hard or impossible to measure directly. If we fit these unknown parameters, fiddling with them until they agree with past experiments, how much can we trust their predictions? We have found that predictions can be made despite huge uncertainties in the parameters many parameter combinations are mostly unimportant to the collective behavior. We will use ideas and methods from differential geometry and approximation theory to explain sloppiness as a hyperribbon structure of the manifold of possible model predictions. We show that physics theories are also sloppy that sloppiness may be the underlying reason why the world is comprehensible. We will present new methods for visualizing this model manifold for probabilistic systems such as the Ising model and the space of possible universes (as measured by the cosmic microwave background radiation). Based on collaboration with Katherine Quinn, Mark Transtrum, Han Kheng Teoh, Ben Machta, Colin Clement, Archishman Raju, Heather Wilber, Ricky Chachra, Ryan Gutenkunst, Joshua J. Waterfall, Fergal P. Casey, Kevin S. Brown, Christopher R. Myers. |
Numéro de preprint arXiv | |
Commentaires | |
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