1 citations · 1 across the 4 of their papers we have counts for
4 papers
CO sequestration hybrid solver using isogeometric alternating-directions and collocation-based robust variational physics informed neural networks (IGA-ADS-CRVPINN)
Askold Vilkha, Tomasz Służalec, Marcin Łoś +1
This paper presents the hybrid solver for a sequestration problem. The solver uses the IGA-ADS (IsoGeometric Analysis Alternating Directions solver) to compute the saturatio…
Python library supporting Discrete Variational Formulations and training solutions with Collocation-based Robust Variational Physics Informed Neural Networks (DVF-CRVPINN)
Tomasz Służalec, Marcin Łoś, Askold Vilkha +1
We explore the possibility of solving Partial Differential Equations (PDEs) using discrete weak formulations. We propose a programming environment for defining a discrete computati…
Inference on neutron star parameters and the nuclear equation of state with RIFT, using prior EOS information
Askold Vilkha, Anjali Yelikar, Richard O'Shaughnessy +1
In this paper, we present an inference method for determining neutron star parameters and constraining the nuclear equation of state (EOS) using the RIFT parameter inference engine…
Collocation-based Robust Variational Physics-Informed Neural Networks (CRVPINN)
Marcin Łoś, Tomasz Służalec, Paweł Maczuga +3
Physics-Informed Neural Networks (PINNs) have been successfully applied to solve Partial Differential Equations (PDEs). Their loss function is founded on a strong residual minimiza…