Sayan Ghosh
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A comprehensive review of digital twin—part 1: modeling and twinning enabling technologies
A Thelen, X Zhang, O Fink, Y Lu, S Ghosh, BD Youn, MD Todd, ...
Structural and Multidisciplinary Optimization 65 (12), 354, 2022
A comprehensive review of digital twin—part 2: roles of uncertainty quantification and optimization, a battery digital twin, and perspectives
A Thelen, X Zhang, O Fink, Y Lu, S Ghosh, BD Youn, MD Todd, ...
Structural and multidisciplinary optimization 66 (1), 1, 2023
Advances in Bayesian Probabilistic Modeling for Industrial Applications
S Ghosh, P Pandita, S Atkinson, W Subber, Y Zhang, NC Kumar, ...
ASME J. Risk Uncertainty Part B 6 (3), 030904 (13 pages), 2020
A Strategy for Adaptive Sampling of Multi-Fidelity Gaussian Processes to Reduce Predictive Uncertainty
S Ghosh, J Kristensen, Y Zhang, W Subber, L Wang
ASME 2019 International Design Engineering Technical Conferences and†…, 2019
Bayesian learning of orthogonal embeddings for multi-fidelity Gaussian Processes
P Tsilifis, P Pandita, S Ghosh, V Andreoli, T Vandeputte, L Wang
Computer Methods in Applied Mechanics and Engineering 386, 114147, 2021
An intelligent sampling framework for multi-objective optimization in high dimensional design space
Y Ling, S Ghosh, I Asher, J Kristensen, K Ryan, L Wang
2018 AIAA/ASCE/AHS/ASC Structures, Structural Dynamics, and Materials†…, 2018
A Gaussian Process Modeling Approach for Fast Robust Design With Uncertain Inputs
KM Ryan, J Kristensen, Y Ling, S Ghosh, I Asher, L Wang
ASME Turbo Expo 2018: Turbomachinery Technical Conference and Exposition†…, 2018
Application of Deep Transfer Learning and Uncertainty Quantification for Process Identification in Powder Bed Fusion
P Pandita, S Ghosh, V Gupta, A Meshkov, L Wang
ASCE-ASME J Risk and Uncert in Engrg Sys Part B Mech Engrg, 2021
Industrial Applications of Intelligent Adaptive Sampling Methods for Multi-Objective Optimization
J Kristensen, W Subber, Y Zhang, S Ghosh, NC Kumar, G Khan, L Wang
Design Engineering and Manufacturing [Working Title], 2019
Covariance Matching Collaborative Optimization for Uncertainty-Based Multidisciplinary Aircraft Design
S Ghosh, CH Lee, DN Mavris
15th AIAA/ISSMO Multidisciplinary Analysis and Optimization Conference, 2014
Bayesian Multi-Source Modeling with Legacy Data
S Ghosh, I Asher, J Kristensen, Y Ling, K Ryan, L Wang
2018 AIAA Non-Deterministic Approaches Conference, 1663, 2018
Rotor Configurational Effect on Rotorcraft Brownout
S Ghosh, MW Lohry, RG Rajagopalan
28th AIAA Applied Aerodynamics Conference, 4238, 2010
A Fully Bayesian Gradient-Free Supervised Dimension Reduction Method using Gaussian Processes
R Gautier, P Pandita, S Ghosh, D Mavris
arXiv preprint arXiv:2008.03534, 2020
Inverse Aerodynamic Design of Gas Turbine Blades using Probabilistic Machine Learning
S Ghosh, G Anantha Padmanabha, C Peng, V Andreoli, S Atkinson, ...
Journal of Mechanical Design 144 (2), 021706, 2021
Remarks for Scaling Up a General Gaussian Process to Model Large Dataset with Sub-models
Y Zhang, J Kristensen, W Subber, S Ghosh, G Khan, L Wang
AIAA Scitech 2020 Forum, 0678, 2020
Learning Uncertainty using Clustering and Local Gaussian Process Regression
Y Zhang, S Ghosh, I Asher, Y Ling, L Wang
AIAA Scitech 2019 Forum, 1730, 2019
Efficient robust design optimization using Gaussian process and intelligent sampling
Y Ling, K Ryan, I Asher, J Kristensen, S Ghosh, L Wang
2018 Multidisciplinary Analysis and Optimization Conference, 4175, 2018
Multi-Source Surrogate Modeling with Bayesian Hierarchical Regression
S Ghosh, RB Jacobs, DN Mavris
17th AIAA Non-Deterministic Approaches Conference, 2015
Data-driven predictive modeling of FeCrAl oxidation
I Roy, S Roychowdhury, B Feng, SK Ravi, S Ghosh, R Umretiya, ...
Materials Letters: X, 100183, 2023
Bayesian-Entropy Gaussian Process for Constrained Metamodeling
Y Wang, Y Gao, Y Liu, S Ghosh, W Subber, P Pandita, L Wang
Reliability Engineering & System Safety, 107762, 2021
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