ModalPINN: an extension of Physics-Informed Neural Networks with enforced truncated Fourier decomposition for periodic flow reconstruction using a limited number of imperfect sensors
Gaetan Raynaud,
Sebastien Houde,
Frederick P. Gosselin
Abstract:We propose a new architecture for Physics-Informed Neural Networks (PINN) specialised for oscillatory phenomena.• The hard-coded truncated Fourier decomposition allows a better precision than the classical approach at an equivalent number of degrees of freedom and computing time.• The proposed format shows great convergence for field reconstruction with data from a limited number of sensors and can overcome problems of time synchronisation and noise.
Set email alert for when this publication receives citations?
scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.