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Piyush Pandita
Piyush Pandita
Staff Engineer, GE Aerospace
Verified email at ge.com
Title
Cited by
Cited by
Year
Process optimization of graphene growth in a roll-to-roll plasma CVD system
MA Alrefae, A Kumar, P Pandita, A Candadai, I Bilionis, TS Fisher
Aip Advances 7 (11), 2017
452017
Advances in bayesian probabilistic modeling for industrial applications
S Ghosh, P Pandita, S Atkinson, W Subber, Y Zhang, NC Kumar, ...
ASCE-ASME Journal of Risk and Uncertainty in Engineering Systems, Part B†…, 2020
372020
Extending expected improvement for high-dimensional stochastic optimization of expensive black-box functions
P Pandita, I Bilionis, J Panchal
Journal of Mechanical Design 138 (11), 111412, 2016
272016
STOCHASTIC MULTIOBJECTIVE OPTIMIZATION ON A BUDGET: APPLICATION TO MULTIPASS WIRE DRAWING WITH QUANTIFIED UNCERTAINTIES
P Pandita, I Bilionis, J Panchal, BP Gautham, A Joshi, P Zagade
International Journal for Uncertainty Quantification 8 (3), 2018
252018
Bayesian Optimal Design of Experiments for Inferring The Statistical Expectation of Expensive Black-Box Functions
P Pandita, I Bilionis, JH Panchal
Journal of Mechanical Design 141 (10), 101404, 2019
242019
A Fully Bayesian Gradient-Free Supervised Dimension Reduction Method using Gaussian Processes
R Gautier, P Pandita, S Ghosh, D Mavris
International Journal for Uncertainty Quantification 12 (2), 2022
232022
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
222021
Application of deep transfer learning and uncertainty quantification for process identification in powder bed fusion
P Pandita, S Ghosh, VK Gupta, A Meshkov, L Wang
ASCE-ASME Journal of Risk and Uncertainty in Engineering Systems, Part B†…, 2022
212022
Surrogate-based sequential Bayesian experimental design using non-stationary Gaussian Processes
P Pandita, P Tsilifis, NM Awalgaonkar, I Bilionis, J Panchal
Computer Methods in Applied Mechanics and Engineering 385, 114007, 2021
202021
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, 2022
172022
Bayesian model calibration and optimization of surfactant-polymer flooding
P Naik, P Pandita, S Aramideh, I Bilionis, AM Ardekani
Computational Geosciences 23, 981-996, 2019
152019
Remarks for scaling up a general gaussian process to model large dataset with sub-models
Y Zhang, S Ghosh, P Pandita, W Subber, G Khan, L Wang
AIAA Scitech 2020 Forum, 0678, 2020
122020
Scalable fully Bayesian Gaussian process modeling and calibration with adaptive sequential Monte Carlo for industrial applications
P Pandita, P Tsilifis, S Ghosh, L Wang
Journal of Mechanical Design 143 (7), 074502, 2021
102021
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 214, 107762, 2021
92021
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
82021
Reinforcement learning-based sequential batch-sampling for bayesian optimal experimental design
Y Ashenafi, P Pandita, S Ghosh
Journal of Mechanical Design 144 (9), 091705, 2022
72022
Efficient bayesian inverse method using robust gaussian processes for design under uncertainty
S Ghosh, P Pandita, W Subber, Y Zhang, L Wang
AIAA Scitech 2020 Forum, 1877, 2020
52020
Data-based discovery of governing equations
W Subber, P Pandita, S Ghosh, G Khan, L Wang, R Ghanem
AAAI-MLPS Symposium 2021, 2020
42020
Towards scalable gaussian process modeling
P Pandita, J Kristensen, L Wang
International Design Engineering Technical Conferences and Computers and†…, 2019
42019
Dimensionality Reduction for Multi-Fidelity Gaussian Processes using Bayesian Adaptation
P Tsilifis, P Pandita, S Ghosh, L Wang
AIAA Scitech 2021 Forum, 1588, 2021
32021
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