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DTSTART:19700308T020000
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BEGIN:VEVENT
DTSTAMP:20210916T132451Z
LOCATION:Henry Dunant
DTSTART;TZID=Europe/Stockholm:20210707T120000
DTEND;TZID=Europe/Stockholm:20210707T123000
UID:submissions.pasc-conference.org_PASC21_sess143_msa276@linklings.com
SUMMARY:Physics Informed Neural Networks for Simulation of PDEs
DESCRIPTION:Minisymposium\n\nPhysics Informed Neural Networks for Simulati
 on of PDEs\n\nMishra\n\nComputer simulations of systems modelled by partia
 l differential equations is a mature field. However, several large scale p
 roblems are currently unfeasible on account of the computational costs. Ma
 chine learning, particularly deep learning techniques are being increasing
 ly used to accelarate or facilitate simutions of PDEs. In this talk, we wi
 ll survey some results on the use of Physics Informed Neural Networks (PIN
 Ns) for solving PDEs. PINNs enable a seamless integration of PDEs, Physics
  and Data and we provide several examples of successful applicationns of P
 INNs for solving forward as well as inverse problems for PDEs. We will als
 o indicate the theoretical basis for the success of PINNs. Finally, we wil
 l discuss possible applications of PINNs in the context of brain tissue mo
 delling.\n\nDomain: CS and Math, Chemistry and Materials, Physics, Life Sc
 iences, Engineering
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