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SUMMARY:Quasinormal Modes in Modified Gravity using Physics-Informed Neura
 l Networks
DTSTART:20250227T143000Z
DTEND:20250227T160000Z
DTSTAMP:20260611T075608Z
UID:edf033b4-9954-4f83-8e01-23249945afbf
SEQUENCE:1
CREATED:20250224T151630Z
DESCRIPTION: We apply a novel approach based on physics-informed neural ne
 tworks to the computation of quasinormal modes of black hole solutions in 
 modified gravity. In particular\, we focus on the case of Einstein-scalar-
 Gauss-Bonnet theory\, with several choices of the coupling function betwee
 n the scalar field and the Gauss-Bonnet invariant. This type of calculatio
 n introduces a number of challenges with respect to the case of General Re
 lativity\, mainly due to the extra complexity of the perturbation equation
 s and to the fact that the background solution is known only numerically. 
 The solution of these perturbation equations typically requires sophistica
 ted numerical techniques that are not easy to develop in computational cod
 es. We show that physics-informed neural networks have an accuracy which i
 s comparable to traditional numerical methods in the case of numerical bac
 kgrounds\, while being very simple to implement. Additionally\, the use of
  GPU parallelization is straightforward thanks to the use of standard mach
 ine learning environments. 
LAST-MODIFIED:20250224T151630Z
LOCATION:DF Seminar Room (2-8.3)\, 2nd floor of Physics Building
URL:http://df.vps.tecnico.ulisboa.pt/en/events/quasinormal-modes-in-modifi
 ed-gravity-using-physics-informed-neural-networks/
X-ALT-DESC;FMTTYPE=text/html:<p data-block-key="8a0r5"> We apply a novel a
 pproach based on physics-informed neural networks to the computation of qu
 asinormal modes of black hole solutions in modified gravity. In particular
 \, we focus on the case of Einstein-scalar-Gauss-Bonnet theory\, with seve
 ral choices of the coupling function between the scalar field and the Gaus
 s-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 tha
 t the background solution is known only numerically. <br/><br/>The solutio
 n of these perturbation equations typically requires sophisticated numeric
 al techniques that are not easy to develop in computational codes. We show
  that physics-informed neural networks have an accuracy which is comparabl
 e to traditional numerical methods in the case of numerical backgrounds\, 
 while being very simple to implement. Additionally\, the use of GPU parall
 elization is straightforward thanks to the use of standard machine learnin
 g environments. </p>
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