@article{gin_petzold_uthappa_neighbors_borough_gin_lashnits_sempowski_denny_bienzle_et al._2023, title={Evaluation of SARS-CoV-2 identification methods through surveillance of companion animals in SARS-CoV-2-positive homes in North Carolina, March to December 2020}, volume={11}, ISSN={["2167-8359"]}, DOI={10.7717/peerj.16310}, abstractNote={We collected oral and/or rectal swabs and serum from dogs and cats living in homes with SARS-CoV-2-PCR-positive persons for SARS-CoV-2 PCR and serology testing. Pre-COVID-19 serum samples from dogs and cats were used as negative controls, and samples were tested in duplicate at different timepoints. Raw ELISA results scrutinized relative to known negative samples suggested that cut-offs for IgG seropositivity may require adjustment relative to previously proposed values, while proposed cut-offs for IgM require more extensive validation. A small number of pet dogs (2/43, 4.7%) and one cat (1/21, 4.8%) were positive for SARS-CoV-2 RNA, and 28.6 and 37.5% of cats and dogs were positive for anti-SARS-CoV-2 IgG, respectively.}, journal={PEERJ}, author={Gin, Taylor E. and Petzold, Elizabeth A. and Uthappa, Diya M. and Neighbors, Coralei E. and Borough, Anna R. and Gin, Craig and Lashnits, Erin and Sempowski, Gregory D. and Denny, Thomas and Bienzle, Dorothee and et al.}, year={2023}, month={Oct} }
 @article{gin_shea_brunton_kutz_2021, title={DeepGreen: deep learning of Green's functions for nonlinear boundary value problems}, volume={11}, ISSN={["2045-2322"]}, DOI={10.1038/s41598-021-00773-x}, abstractNote={Abstract Boundary value problems (BVPs) play a central role in the mathematical analysis of constrained physical systems subjected to external forces. Consequently, BVPs frequently emerge in nearly every engineering discipline and span problem domains including fluid mechanics, electromagnetics, quantum mechanics, and elasticity. The fundamental solution, or Green’s function, is a leading method for solving linear BVPs that enables facile computation of new solutions to systems under any external forcing. However, fundamental Green’s function solutions for nonlinear BVPs are not feasible since linear superposition no longer holds. In this work, we propose a flexible deep learning approach to solve nonlinear BVPs using a dual-autoencoder architecture. The autoencoders discover an invertible coordinate transform that linearizes the nonlinear BVP and identifies both a linear operator L and Green’s function G which can be used to solve new nonlinear BVPs. We find that the method succeeds on a variety of nonlinear systems including nonlinear Helmholtz and Sturm–Liouville problems, nonlinear elasticity, and a 2D nonlinear Poisson equation and can solve nonlinear BVPs at orders of magnitude faster than traditional methods without the need for an initial guess. The method merges the strengths of the universal approximation capabilities of deep learning with the physics knowledge of Green’s functions to yield a flexible tool for identifying fundamental solutions to a variety of nonlinear systems.}, number={1}, journal={SCIENTIFIC REPORTS}, author={Gin, Craig R. and Shea, Daniel E. and Brunton, Steven L. and Kutz, J. Nathan}, year={2021}, month={Nov} }
 @article{gin_daripa_2021, title={Time-dependent injection strategies for multilayer Hele-Shaw and porous media flows}, volume={6}, ISSN={["2469-990X"]}, DOI={10.1103/PhysRevFluids.6.033901}, abstractNote={Universality in the behavior of multilayer radial Hele-Shaw flows is discovered by semi-analytical methods. In particular, it is found numerically that the maximum injection rate for a stable flow decreases proportional to ${t}^{\ensuremath{-}1/3}$ regardless of the number of interfaces and increases at a rate proportional to the number of interfaces to the two-thirds power at large time $t\ensuremath{\gg}1$. However, at earlier times the number of interfaces can increase the maximum stable injection rate by a much greater amount.}, number={3}, journal={PHYSICAL REVIEW FLUIDS}, author={Gin, Craig and Daripa, Prabir}, year={2021}, month={Mar} }