| 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 | 255 | 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 | 115 | 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 | 42 | 2020 |
| 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 | 27 | 2020 |
| 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 | 26 | 2021 |
| 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 | 25 | 2021 |
| 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 | 25 | 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 | 24 | 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 | 20 | 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 | 19 | 2018 |
| Elucidating Precipitation in FeCrAl Alloys through Explainable AI: A Case Study I Roy, SK Ravi, S Roychowdhury, B Feng, S Ghosh, C Reynolds, ... | 17 | 2022 |
| 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 | 17 | 2019 |
| Understanding oxidation of Fe-Cr-Al alloys through explainable artificial intelligence I Roy, B Feng, S Roychowdhury, SK Ravi, RV Umretiya, C Reynolds, ... MRS communications, 1-7, 2023 | 16 | 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 | 16 | 2021 |
| Data-driven predictive modeling of FeCrAl oxidation I Roy, S Roychowdhury, B Feng, SK Ravi, S Ghosh, R Umretiya, ... Materials Letters: X, 100183, 2023 | 15 | 2023 |
| On Uncertainty Quantification in Materials Modeling and Discovery: Applications of GE's BHM and IDACE SK Ravi, A Bhaduri, A Amer, S Ghosh, L Wang, A Hoffman, R Umretiya, ... AIAA SCITECH 2023 Forum, 2023 | 15 | 2023 |
| 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 | 15 | 2018 |
| 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 | 15 | 2014 |
| Rotor Configurational Effect on Rotorcraft Brownout S Ghosh, MW Lohry, RG Rajagopalan 28th AIAA Applied Aerodynamics Conference, 4238, 2010 | 14 | 2010 |
| Pro-ML IDeAS: A Probabilistic Framework for Explicit Inverse Design using Invertible Neural Network S Ghosh, GA Padmanabha, C Peng, S Atkinson, V Andreoli, P Pandita, ... AIAA Scitech 2021 Forum, 0465, 2021 | 12 | 2021 |
Piyush PanditaStaff Engineer, GE AerospaceBestätigte E-Mail-Adresse bei ge.com
