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SUMMARY:Predicting the mechanical properties of disordered materials by ex
 ploiting Bayesian Machine Learning
DTSTART:20261012T133000Z
DTEND:20261012T143000Z
DTSTAMP:20261001T223500Z
UID:indico-event-1913@indico.dfa.unipd.it
CONTACT:marco.baiesi@unipd.it
DESCRIPTION:Speakers: Mikko Alava (Aalto University\, Finland)\n\nPredicti
 ng the mechanical response of disordered materials is challenging because 
 structure-property relations are noisy\, datasets are often small\, and yi
 elding or failure may emerge abruptly. I discuss how Bayesian machine lear
 ning can turn these limitations into uncertainty-aware predictions. First\
 , rheological measurements provide a low-dimensional route to predict and 
 optimize the mechanical properties of bio-based foams. I then present unpu
 blished work extending Bayesian inference to complete foam stress-strain b
 ehavior. For metallic glasses\, Bayesian optimization explores composition
  space for target mechanical properties\, while physics-informed Bayesian 
 inference predicts plastic-strain growth and yielding from the early mecha
 nical response. Finally\, evolving activation barrier landscapes inferred 
 from acoustic-emission data allow creep lifetime to be predicted well befo
 re failure.Selected papers:1. I. Y. Miranda-Valdez et al.\, "Accelerated d
 esign of solid bio-based foams for plastics substitutes\," Materials Horiz
 ons 12\, 1855-1862 (2025).2. T. Makinen\, A. D. S. Parmar\, S. Bonfanti & 
 M. J. Alava\, "Bayesian exploration of the composition space of CuZrAl met
 allic glasses for mechanical properties\," npj Computational Materials 11\
 , 96 (2025).3. T. Makinen\, A. D. S. Parmar\, S. Bonfanti & M. J. Alava\, 
 "Growth and prediction of plastic strain in metallic glasses\," Physical R
 eview Materials 10\, 025601 (2026).4. J. C. Verano-Espitia\, T. Makinen\, 
 M. J. Alava & J. Weiss\, "Early Prediction of Creep Failure via Bayesian I
 nference of Evolving Barriers\," arXiv:2603.16419 (2026).In collaboration 
 at Aalto with: Isaac Y. Miranda-Valdez\, Tero Makinen\, Juha Koivisto\,  
 Juan Carlos Verano-Espitia\, Jerome Weiss\n\nhttps://indico.dfa.unipd.it/e
 vent/1913/
LOCATION:P2B (Dipartimento di Fisica e Astronomia - Edificio Ricci-Curbast
 ro (ex-Paolotti))
URL:https://indico.dfa.unipd.it/event/1913/
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