His primary areas of investigation include Density functional theory, Composite material, Monolayer, Zigzag and Graphene. His Density functional theory research includes themes of Elastic energy and Condensed matter physics. His study focuses on the intersection of Monolayer and fields such as Ultimate tensile strength with connections in the field of Nanomaterials, Atom and Radiation damage.
His Zigzag research is multidisciplinary, incorporating elements of Stiffness and Deformation. His Stiffness course of study focuses on Poisson's ratio and Modulus. His Graphene study improves the overall literature in Nanotechnology.
Suvranu De spends much of his time researching Simulation, Haptic technology, Finite element method, Virtual reality and Trainer. As part of his studies on Simulation, Suvranu De often connects relevant areas like Laparoscopic surgery. His Haptic technology study combines topics from a wide range of disciplines, such as Imaging phantom, Torque, Soft tissue and Rendering.
His Finite element method research includes elements of Algorithm, Mathematical optimization, Computational science and Nonlinear system. His research in Virtual reality focuses on subjects like Endoscopic surgery, which are connected to Medical physics. His Trainer study combines topics in areas such as Surgery, Laparoscopic skill and Face validity.
Artificial intelligence, Motor skill, Machine learning, Neuroimaging and Trainer are his primary areas of study. Many of his studies on Artificial intelligence involve topics that are commonly interrelated, such as Computer vision. The study incorporates disciplines such as Multiscale modeling and Process in addition to Machine learning.
His biological study spans a wide range of topics, including Test, Simulation, Pairwise comparison, Virtual reality and Face validity. Suvranu De has researched Simulation in several fields, including Content validity and Escharotomy. His work on Surgical simulator as part of his general Virtual reality study is frequently connected to Experience level, thereby bridging the divide between different branches of science.
His primary areas of study are Artificial intelligence, Simulation, Trainer, Virtual reality and Learning curve. His research integrates issues of Parametric statistics, Finite element method, Computational science, Preconditioner and Machine learning in his study of Artificial intelligence. His Simulation research is multidisciplinary, incorporating perspectives in Laparoscopic surgery, Laparoscopic skill and Surgical training.
Laparoscopic surgery is a subfield of Surgery that Suvranu De tackles. His Trainer research focuses on Face validity and how it connects with Wilcoxon signed-rank test, Haptic technology, Needle insertion, Significant difference and Content validity. Borrowing concepts from Convergent validity, Suvranu De weaves in ideas under Virtual reality.
This overview was generated by a machine learning system which analysed the scientist’s body of work. If you have any feedback, you can contact us here.
Haptics in minimally invasive surgical simulation and training
C. Basdogan;S. De;J. Kim;Manivannan Muniyandi.
IEEE Computer Graphics and Applications (2004)
THE METHOD OF FINITE SPHERES
S. De;K. J. Bathe.
Computational Mechanics (2000)
Mechanical properties of the hexagonal boron nitride monolayer: Ab initio study
Qing Peng;Wei Ji;Suvranu De.
Computational Materials Science (2012)
Effects of insertion conditions on tissue strain and vascular damage during neuroprosthetic device insertion.
C. S. Bjornsson;Seung Jae Oh;Y. A. Al-Kofahi;Y. J. Lim.
Journal of Neural Engineering (2006)
New materials graphyne, graphdiyne, graphone, and graphane: review of properties, synthesis, and application in nanotechnology
Qing Peng;Albert K Dearden;Jared Crean;Liang Han.
Nanotechnology, Science and Applications (2014)
Outstanding mechanical properties of monolayer MoS2 and its application in elastic energy storage
Qing Peng;Suvranu De.
Physical Chemistry Chemical Physics (2013)
Mechanical properties of graphyne monolayers: a first-principles study
Qing Peng;Wei Ji;Suvranu De.
Physical Chemistry Chemical Physics (2012)
Integrating machine learning and multiscale modeling—perspectives, challenges, and opportunities in the biological, biomedical, and behavioral sciences
Mark Alber;Adrian Buganza Tepole;William R. Cannon;Suvranu De.
npj Digital Medicine (2019)
The method of finite spheres with improved numerical integration
Suvranu De;Klaus-Jürgen Bathe.
Computers & Structures (2001)
Common uses and cited complications of energy in surgery
Ganesh Sankaranarayanan;Rajeswara R Resapu;Daniel B Jones;Steven D Schwaitzberg;Steven D Schwaitzberg.
Surgical Endoscopy and Other Interventional Techniques (2013)
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