| 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 | 48 | 2017 |
| 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 | 42 | 2020 |
| 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 | 28 | 2016 |
| 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 | 27 | 2022 |
| 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 | 26 | 2019 |
| 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 | 26 | 2018 |
| 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 | 25 | 2022 |
| 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 | 24 | 2021 |
| 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 | 22 | 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, 2022 | 20 | 2022 |
| 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 | 14 | 2021 |
| 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 | 14 | 2019 |
| 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 | 12 | 2020 |
| 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 | 11 | 2021 |
| 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 | 10 | 2021 |
| Probabilistic transfer learning through ensemble probabilistic deep neural network SK Ravi, P Pandita, S Ghosh, A Bhaduri, V Andreoli, L Wang AIAA SCITECH 2023 Forum, 1479, 2023 | 7 | 2023 |
| 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 | 7 | 2022 |
| 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 | 5 | 2020 |
| Towards scalable gaussian process modeling P Pandita, J Kristensen, L Wang International Design Engineering Technical Conferences and Computers and …, 2019 | 5 | 2019 |
| Data-based discovery of governing equations W Subber, P Pandita, S Ghosh, G Khan, L Wang, R Ghanem AAAI-MLPS Symposium 2021, 2020 | 4 | 2020 |
Sayan GhoshSenior Engineer at GE Aerospace ResearchVerified email at ge.com