<div class="csl-bib-body">
<div class="csl-entry">Wallerberger, M. (2025, August 22). <i>Navigating the bias-variance tradeoff in materials science</i> [Conference Presentation]. Joint Annual Meeting of the Austrian and Swiss Physical Society (ÖPG-SPS 2025), Wien, Austria. https://doi.org/10.34726/11827</div>
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dc.identifier.uri
http://hdl.handle.net/20.500.12708/226212
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dc.identifier.uri
https://doi.org/10.34726/11827
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dc.description.abstract
In this introductory talk, I aim to give a broad overview over machine learning (ML) and related techniques as they have been applied to problems in materials science. The fundamental tradeoff between uncertainty and bias inherent to learning shall serve as our guide. It explains why large-scale general purpose models, while valuable, have had limited impact in physics, and why on the other hand simple ML models tailored to specific problems have led to breakthroughs to molecular dynamics and quantum field theories. Time permitting, we will turn to physical interpretations and phase transitions in the tradeoff itself.
en
dc.description.sponsorship
FWF - Österr. Wissenschaftsfonds
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dc.language.iso
en
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dc.rights.uri
http://creativecommons.org/licenses/by-sa/4.0/
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dc.subject
materials science
en
dc.title
Navigating the bias-variance tradeoff in materials science
en
dc.type
Presentation
en
dc.type
Vortrag
de
dc.rights.license
Creative Commons Namensnennung - Weitergabe unter gleichen Bedingungen 4.0 International
de
dc.rights.license
Creative Commons Attribution-ShareAlike 4.0 International
en
dc.identifier.doi
10.34726/11827
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dc.relation.grantno
P 36332-N
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dc.type.category
Conference Presentation
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tuw.publication.invited
invited
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tuw.project.title
Sparse modeling for 2P response and parquet equations
