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SUMMARY:Quasinormal Modes in Modified Gravity using Physics-Informed Neura
 l Networks
DTSTART:20250227T143000Z
DTEND:20250227T160000Z
DTSTAMP:20260422T114554Z
UID:edf033b4-9954-4f83-8e01-23249945afbf
SEQUENCE:2
CREATED:20250224T151641Z
DESCRIPTION:We apply a novel approach based on physics-informed neural net
 works to the computation of quasinormal modes of black hole solutions in m
 odified gravity. In particular\, we focus on the case of Einstein-scalar-G
 auss-Bonnet theory\, with several choices of the coupling function between
  the scalar field and the Gauss-Bonnet invariant. This type of calculation
  introduces a number of challenges with respect to the case of General Rel
 ativity\, mainly due to the extra complexity of the perturbation equations
  and to the fact that the background solution is known only numerically. T
 he solution of these perturbation equations typically requires sophisticat
 ed numerical techniques that are not easy to develop in computational code
 s. We show that physics-informed neural networks have an accuracy which is
  comparable to traditional numerical methods in the case of numerical back
 grounds\, while being very simple to implement. Additionally\, the use of 
 GPU parallelization is straightforward thanks to the use of standard machi
 ne learning environments.
LAST-MODIFIED:20250224T151652Z
LOCATION:DF Seminar Room (2-8.3)\, 2nd floor of Physics Building
URL:http://df.vps.tecnico.ulisboa.pt/pt/eventos/quasinormal-modes-in-modif
 ied-gravity-using-physics-informed-neural-networks/
X-ALT-DESC;FMTTYPE=text/html:<p data-block-key="8a0r5">We apply a novel ap
 proach based on physics-informed neural networks to the computation of qua
 sinormal modes of black hole solutions in modified gravity. In particular\
 , we focus on the case of Einstein-scalar-Gauss-Bonnet theory\, with sever
 al choices of the coupling function between the scalar field and the Gauss
 -Bonnet invariant.<br/><br/> This type of calculation introduces a number 
 of challenges with respect to the case of General Relativity\, mainly due 
 to the extra complexity of the perturbation equations and to the fact that
  the background solution is known only numerically.<br/><br/> The solution
  of these perturbation equations typically requires sophisticated numerica
 l techniques that are not easy to develop in computational codes. We show 
 that physics-informed neural networks have an accuracy which is comparable
  to traditional numerical methods in the case of numerical backgrounds\, w
 hile being very simple to implement. Additionally\, the use of GPU paralle
 lization is straightforward thanks to the use of standard machine learning
  environments.</p>
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