J
John Gounley
Researcher at Oak Ridge National Laboratory
Publications - 42
Citations - 503
John Gounley is an academic researcher from Oak Ridge National Laboratory. The author has contributed to research in topics: Computer science & Lattice Boltzmann methods. The author has an hindex of 9, co-authored 34 publications receiving 242 citations. Previous affiliations of John Gounley include Old Dominion University & Aix-Marseille University.
Papers
More filters
Journal ArticleDOI
Influence of surface viscosity on droplets in shear flow
TL;DR: In this paper, the behavior of a single droplet in an immiscible external fluid, submitted to shear flow is investigated using numerical simulations, where the surface of the droplet is modelled by a Boussinesq-Scriven constitutive law involving the interfacial viscosities and a constant surface tension.
Journal ArticleDOI
Limitations of Transformers on Clinical Text Classification
Shang Gao,Mohammed Alawad,M. Todd Young,John Gounley,Noah Schaefferkoetter,Hong-Jun Yoon,Xiao-Cheng Wu,Eric B. Durbin,Jennifer A. Doherty,Antoinette M. Stroup,Linda Coyle,Georgia D. Tourassi +11 more
TL;DR: In this article, the authors introduce four methods to scale BERT, which by default can only handle input sequences up to approximately 400 words long, to perform document classification on clinical texts several thousand words long.
Journal ArticleDOI
The importance of side branches in modeling 3D hemodynamics from angiograms for patients with coronary artery disease.
TL;DR: It is demonstrated that models based on accurate coronary physiology can improve overall fidelity of biomechanical studies to compute hemodynamic risk-factors and shows that models that take into account flow through all side branches are required for precise computation of shear stress and pressure gradient.
Journal ArticleDOI
Numerical simulation of a compound capsule in a constricted microchannel.
TL;DR: This study extends a parallel hemodynamics application to simulate the fluid-structure interaction between compound capsules and fluid, and compares the deformation of simple and compound capsules in constricted microchannels and explores how deformation depends on the capillary number and on the volume fraction of the inner membrane.
Journal ArticleDOI
Accelerating massively parallel hemodynamic models of coarctation of the aorta using neural networks
Bradley Feiger,John Gounley,Dale Adler,Jane A. Leopold,Erik W. Draeger,Rafeed Chaudhury,Justin Ryan,Girish Pathangey,Kevin Winarta,David H. Frakes,Franziska Michor,Amanda Randles +11 more
TL;DR: A framework is developed to identify the minimal training set required to build a predictive model on a per-patient basis for machine learning-driven, physics-based simulations to predict the effects of physiological factors on hemodynamics in patients with coarctation of the aorta.