14 citations · 22 across the 8 of their papers we have counts for
8 papers
Mathematical Modeling, Analysis and Simulation Utilizing Machine Learning Tools for Assessing the Impact of Climate Lobbying
Andrew Jacoby, Samiran Ghosh, Malay Banerjee +2
Climate policy and legislation has a significant influence on both domestic and global responses to the pressing environmental challenges of our time. The effectiveness of such cli…
Modeling COVID-19 spread in the USA using metapopulation SIR models coupled with graph convolutional neural networks
Petr Kisselev, Padmanabhan Seshaiyer
Graph convolutional neural networks (GCNs) have shown tremendous promise in addressing data-intensive challenges in recent years. In particular, some attempts have been made to imp…
A Generalized Weak Galerkin method for Oseen equation
Wenya Qi, Padmanabhan Seshaiyer, Junping Wang
In this work, the authors introduce a generalized weak Galerkin (gWG) finite element method for the time-dependent Oseen equation. The generalized weak Galerkin method is based on…
Generalized Weak Galerkin Methods For Stokes Equations
W. Qi, P. Seshaiyer, J. Wang
A new weak Galerkin finite element method, called generalized weak Galerkin method ({g}WG), is introduced for Stokes equations in this paper by using a new definition of the weak g…
Data-driven approaches for predicting spread of infectious diseases through DINNs: Disease Informed Neural Networks
Sagi Shaier, Maziar Raissi, Padmanabhan Seshaiyer
In this work, we present an approach called Disease Informed Neural Networks (DINNs) that can be employed to effectively predict the spread of infectious diseases. This approach bu…
Finite element method with the total stress variable for Biot's consolidation model
Wenya Qi, Padmanabhan Seshaiyer, Junping Wang
In this work, semi-discrete and fully-discrete error estimates are derived for the Biot's consolidation model described using a three-field finite element formulation. The fields i…
A multi-fidelity stochastic collocation method for parabolic PDEs with random input data
Maziar Raissi, Padmanabhan Seshaiyer
Over the last few years there have been dramatic advances in our understanding of mathematical and computational models of complex systems in the presence of uncertainty. This has…
A multi-fidelity stochastic collocation method using locally improved reduced-order models
Maziar Raissi, Padmanabhan Seshaiyer
Over the last few years there have been dramatic advances in our understanding of mathematical and computational models of complex systems in the presence of uncertainty. This has…