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X-WR-CALNAME:The Synergy between Machine Learning and the Natural Sciences
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UID:2024-02-20-max-welling@cml.ics.uci.edu
DTSTAMP:20240220T000000Z
SEQUENCE:57690
DTSTART;TZID=America/Los_Angeles:20240220T140000
DTEND;TZID=America/Los_Angeles:20240220T150000
SUMMARY:[CML Seminar] Max Welling: The Synergy between Machine Learning and
the Natural Sciences
LOCATION:Donald Bren Hall 6011
DESCRIPTION:Max Welling\, Professor and Research Chair in Machine Learning\
, University of Amsterdam\n\nTitle: The Synergy between Machine Learning a
nd the Natural Sciences\n\nAbstract: Traditionally machine learning has be
en heavily influenced by neuroscience (hence the name artificial neural ne
tworks) and physics (e.g. MCMC\, Belief Propagation\, and Diffusion based
Generative AI). We have recently witnessed that the flow of information ha
s also reversed\, with new tools developed in the ML community impacting p
hysics\, chemistry and biology. Examples include faster DFT\, Force-Field
accelerated MD simulations\, PDE Neural Surrogate models\, generating drug
like molecules\, and many more. In this talk I will review the exciting op
portunities for further cross fertilization between these fields\, ranging
from faster (classical) DFT calculations and enhanced transition path sam
pling to traveling waves in artificial neural networks.\n\nhttps://cml.ics
.uci.edu/seminars/2024-02-20-max-welling
X-ALT-DESC;FMTTYPE=text/html:Max Welling\, Professor and
Research Chair in Machine Learning\, University of Amsterdam
Ti
tle: The Synergy between Machine Learning and the Natural Sciences
Abstract: Traditionally machine learning has been heavily influ
enced by neuroscience (hence the name artificial neural networks) and phys
ics (e.g. MCMC\, Belief Propagation\, and Diffusion based Generative AI).
We have recently witnessed that the flow of information has also reversed\
, with new tools developed in the ML community impacting physics\, chemist
ry and biology. Examples include faster DFT\, Force-Field accelerated MD s
imulations\, PDE Neural Surrogate models\, generating druglike molecules\,
and many more. In this talk I will review the exciting opportunities for
further cross fertilization between these fields\, ranging from faster (cl
assical) DFT calculations and enhanced transition path sampling to traveli
ng waves in artificial neural networks.
https://cml.ics.uci.edu/seminars/20
24-02-20-max-welling
URL:https://cml.ics.uci.edu/seminars/2024-02-20-max-welling
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