Arun Karthi Subramaniyan
Arun Karthi Subramaniyan
GE Global Research Center
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Cited by
Cited by
Continuum interpretation of virial stress in molecular simulations
AK Subramaniyan, CT Sun
International Journal of Solids and Structures 45 (14-15), 4340-4346, 2008
Enhancing compressive strength of unidirectional polymeric composites using nanoclay
AK Subramaniyan, CT Sun
Composites Part A: Applied Science and Manufacturing 37 (12), 2257-2268, 2006
Validation of a peridynamic model for fatigue cracking
G Zhang, Q Le, A Loghin, A Subramaniyan, F Bobaru
Engineering Fracture Mechanics 162, 76-94, 2016
Toughening polymeric composites using nanoclay: crack tip scale effects on fracture toughness
AK Subramaniyan, CT Sun
Composites Part A: applied science and manufacturing 38 (1), 34-43, 2007
Generating Recommendations Based on Semantic Knowledge Capture
P Jethwa, AK Subramaniyan, AN Iankoulski
US Patent App. 16/162,783, 2019
A survey of Bayesian calibration and physics-informed neural networks in scientific modeling
FAC Viana, AK Subramaniyan
Archives of Computational Methods in Engineering 28 (5), 3801-3830, 2021
Generating natural language recommendations based on an industrial language model
X Zhu, AK Subramaniyan, H Zhao, XU Zhengjie
US Patent 10,872,204, 2020
Interlaminar fracture behavior of nanoclay reinforced glass fiber composites
AK Subramaniyan, CT Sun
Journal of Composite Materials 42 (20), 2111-2122, 2008
Improving high-dimensional physics models through Bayesian calibration with uncertain data
NC Kumar, AK Subramaniyan, L Wang
Turbo Expo: Power for Land, Sea, and Air 44731, 407-416, 2012
Calibrating transient models with multiple responses using Bayesian inverse techniques
NC Kumar, AK Subramaniyan, L Wang, G Wiggs
Turbo Expo: Power for Land, Sea, and Air 55263, V07AT28A007, 2013
Challenges in uncertainty, calibration, validation and predictability of engineering analysis models
L Wang, X Fang, A Subramaniyan, G Jothiprasad, M Gardner, A Kale, ...
Turbo Expo: Power for Land, Sea, and Air 54662, 747-758, 2011
The effect of grid resolution and reaction models in simulation of a fluidized bed gasifier through nonintrusive uncertainty quantification techniques
M Shahnam, A Gel, JF Dietiker, AK Subramaniyan, J Musser
Journal of Verification, Validation and Uncertainty Quantification 1 (4), 041004, 2016
Effect of nanoclay on compressive strength of glass fiber composites
AK Subramaniyan, Q Bing, D Nakima, CT Sun
CD Proceedings of the 18th Annual Technical Conference of American Society …, 2003
Nonintrusive uncertainty quantification of computational fluid dynamics simulations of a bench-scale fluidized-bed gasifier
A Gel, M Shahnam, J Musser, AK Subramaniyan, JF Dietiker
Industrial & Engineering Chemistry Research 55 (48), 12477-12490, 2016
Engineering molecular mechanics: an efficient static high temperature molecular simulation technique
AK Subramaniyan, CT Sun
Nanotechnology 19 (28), 285706, 2008
Methods and systems for implementing a data reconciliation framework
IM Asher, AR Cerrone, Y Ling, A Srivastava, AK Subramaniyan, F Viana, ...
US Patent 10,394,770, 2019
Enhancing high-dimensional physics models for accurate predictions with bayesian calibration
AK Subramaniyan, NC Kumar, L Wang, D Beeson, G Wiggs
Propulsion-Safety and Affordable Readiness Conference, March, 2012
Quantifying uncertainty of a reacting multiphase flow in a bench-scale fluidized bed gasifier: A Bayesian approach
A Gel, M Shahnam, AK Subramaniyan
Powder Technology 311, 484-495, 2017
Hybrid bayesian solution to NASA langley research center multidisciplinary uncertainty quantification challenge
A Srivastava, AK Subramaniyan, L Wang
Journal of Aerospace Information Systems 12 (1), 114-139, 2015
Analytical global sensitivity analysis with Gaussian processes
A Srivastava, AK Subramaniyan, L Wang
AI EDAM 31 (3), 235-250, 2017
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