6 citations · 6 across the 3 of their papers we have counts for
3 papers
PINNGraPE: Physics Informed Neural Network for Gravitational wave Parameter Estimation
Leigh Smith, Matteo Scialpi, Francesco di Clemente +1
Weakly-modelled searches for gravitational waves are essential for ensuring that all potential sources are accounted for in detection efforts, as they make minimal assumptions rega…
APRIL: Auxiliary Physically-Redundant Information in Loss -- A physics-informed framework for parameter estimation with a gravitational-wave case study
Matteo Scialpi, Francesco Di Clemente, Leigh Smith +1
Physics-Informed Neural Networks (PINNs) embed the partial differential equations (PDEs) governing the system under study directly into the training of Neural Networks, ensuring so…
Explainable autoencoder for neutron star dense matter parameter estimation
Francesco Di Clemente, Matteo Scialpi, Michał Bejger
We present a physics-informed autoencoder designed to encode the equation of state of neutron stars into an interpretable latent space. In particular the input will be encoded in t…