A
Adarsh Krishnamurthy
Researcher at Iowa State University
Publications - 96
Citations - 1303
Adarsh Krishnamurthy is an academic researcher from Iowa State University. The author has contributed to research in topics: Computer science & Deep learning. The author has an hindex of 15, co-authored 82 publications receiving 958 citations. Previous affiliations of Adarsh Krishnamurthy include Indian Institute of Technology Madras & University of California.
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Journal ArticleDOI
Patient-specific models of cardiac biomechanics
Adarsh Krishnamurthy,Christopher T. Villongco,Joyce Chuang,Lawrence R. Frank,Vishal Nigam,Ernest Belezzuoli,Paul Stark,David E. Krummen,Sanjiv M. Narayan,Jeffrey H. Omens,Andrew D. McCulloch,Roy C. P. Kerckhoffs +11 more
TL;DR: New methods for creating three-dimensional patient-specific models of ventricular biomechanics in the failing heart showed good agreement with measured echocardiographic and global functional parameters such as ejection fraction and peak cavity pressures.
Journal ArticleDOI
Patient-specific modeling of dyssynchronous heart failure: a case study.
Jazmin Aguado-Sierra,Adarsh Krishnamurthy,Christopher T. Villongco,Joyce Chuang,Elliot J. Howard,Matthew J. Gonzales,Jeffrey H. Omens,David E. Krummen,David E. Krummen,Sanjiv M. Narayan,Sanjiv M. Narayan,Roy C. P. Kerckhoffs,Andrew D. McCulloch +12 more
TL;DR: Some of the remaining challenges in developing reliable patient-specific models of cardiac electromechanical activity are discussed, and some of the main areas for focusing future research efforts are identified.
Journal ArticleDOI
Direct immersogeometric fluid flow analysis using B-rep CAD models
TL;DR: A new method for immersogeometric fluid flow analysis that directly uses the CAD boundary representation (B-rep) of a complex object and immerses it into a locally refined, non-boundary-fitted discretization of the fluid domain and demonstrates the effectiveness of the method for high-fidelity industrial scale simulations.
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A framework for parametric design optimization using isogeometric analysis
Austin J. Herrema,Nelson M. Wiese,Nelson M. Wiese,Carolyn N. Darling,Baskar Ganapathysubramanian,Adarsh Krishnamurthy,Ming-Chen Hsu +6 more
TL;DR: A novel approach that employs IGA methodologies while still rigorously abiding by the paradigms of advanced design parameterization, analysis model validity, and interactivity is proposed, demonstrating the framework’s effectiveness on both an internally pressurized tube and a wind turbine blade.
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Learning localized features in 3D CAD models for manufacturability analysis of drilled holes
TL;DR: A 3D-CNN based gradient-weighted class activation mapping (3D-GradCAM) method that can provide visual explanations of the local geometric features of interest within an object is developed.