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SUMMARY:Modeling (nuclear) quantum many-body systems with neural network q
 uantum states
DTSTART:20230620T120000
DTEND:20230620T140000
DTSTAMP:20260507T015203Z
UID:95a8989bbab800296020ef039efe7abcb03f91fbf3c0f45fdfb7faca
CATEGORIES:Conferences - Seminars
DESCRIPTION:Alessandro Lovato (Argonne National Laboratory\, Illinois\, US
 A)\nI will discuss the application of artificial neural networks as a flex
 ible tool for compactly representing quantum many-body states of systems c
 haracterized by non-perturbative interactions\, such as atomic nuclei and 
 neutron-star matter. This approach offers a systematically improvable solu
 tion with a polynomial cost in the number of nucleons\, allowing us to stu
 dy emergent properties of nuclear systems\, including superfluidity\, clus
 tering\, and shell structures. I will also discuss the potential implicati
 ons of this method for neutrino physics and gravitational-wave astronomy. 
 In addition to the nuclear many-body problem\, we will showcase applicatio
 ns of neural network quantum states in condensed-matter systems\, such as 
 the homogeneous electron gas and strongly interacting cold Fermi gases.\n\
 n\n 
LOCATION:BSP 727 https://plan.epfl.ch/?room==BSP%20727
STATUS:CONFIRMED
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