| Predicting Young's modulus of oxide glasses with sparse datasets using machine learning S Bishnoi, S Singh, R Ravinder, M Bauchy, NN Gosvami, H Kodamana, ... Journal of Non-Crystalline Solids 524, 119643, 2019 | 83 | 2019 |
| Gaussian process modelling with Gaussian mixture likelihood A Daemi, H Kodamana, B Huang Journal of Process Control 81, 209-220, 2019 | 64 | 2019 |
| Process monitoring using a generalized probabilistic linear latent variable model R Raveendran, H Kodamana, B Huang Automatica 96, 73-83, 2018 | 62 | 2018 |
| Deep learning aided rational design of oxide glasses R Ravinder, KH Sridhara, S Bishnoi, HS Grover, M Bauchy, H Kodamana, ... Materials horizons 7 (7), 1819-1827, 2020 | 59 | 2020 |
| Approaches to robust process identification: A review and tutorial of probabilistic methods H Kodamana, B Huang, R Ranjan, Y Zhao, R Tan, N Sammaknejad Journal of Process Control 66, 68-83, 2018 | 58 | 2018 |
| A gap metric based multiple model approach for nonlinear switched systems K Hariprasad, S Bhartiya, RD Gudi Journal of process control 22 (9), 1743-1754, 2012 | 49 | 2012 |
| Reinforcement learning based optimization of process chromatography for continuous processing of biopharmaceuticals N Saxena, A Tiwari, D Sonawat, H Kodamana, AS Rathore Chemical Engineering Science 230, 116171, 2020 | 45* | 2020 |
| Mixtures of Probabilistic PCA With Common Structure Latent Bases for Process Monitoring H Kodamana, R Raveendran, B Huang IEEE Transactions on Control Systems Technology, 2018 | 44 | 2018 |
| Twin actor twin delayed deep deterministic policy gradient (TATD3) learning for batch process control T Joshi, S Makker, H Kodamana, H Kandath Computers & Chemical Engineering 155, 107527, 2021 | 42* | 2021 |
| Multi-objective dynamic optimization of hybrid renewable energy systems R Sharma, H Kodamana, M Ramteke Chemical Engineering and Processing-Process Intensification 170, 108663, 2022 | 41 | 2022 |
| Semi‐supervised dynamic latent variable modeling: I/O probabilistic slow feature analysis approach L Fan, H Kodamana, B Huang AIChE Journal 65 (3), 964-979, 2019 | 41 | 2019 |
| Prediction of ENSO beyond spring predictability barrier using deep convolutional LSTM networks M Gupta, H Kodamana, S Sandeep IEEE Geoscience and Remote Sensing Letters 19, 1-5, 2020 | 39 | 2020 |
| Scalable Gaussian processes for predicting the optical, physical, thermal, and mechanical properties of inorganic glasses with large datasets S Bishnoi, R Ravinder, HS Grover, H Kodamana, NMA Krishnan Materials advances 2 (1), 477-487, 2021 | 38* | 2021 |
| A novel approach to process operating mode diagnosis using conditional random fields in the presence of missing data M Fang, H Kodamana, B Huang, N Sammaknejad Computers & Chemical Engineering 111, 149-163, 2018 | 33 | 2018 |
| Identification of robust probabilistic slow feature regression model for process data contaminated with outliers L Fan, H Kodamana, B Huang Chemometrics and Intelligent Laboratory Systems, 2018 | 33 | 2018 |
| Robust Identification of Nonlinear Errors-in-variables Systems with Parameter Uncertainties Using Variational Bayesian Approach F Guo, H Kodamana, Y Zhao, B Huang, Y Ding IEEE Transactions on Industrial Informatics, 2017 | 32 | 2017 |
| A stabilizing sub-optimal model predictive control for quasi-linear parameter varying systems S Mate, H Kodamana, S Bhartiya, PSV Nataraj IEEE Control Systems Letters 4 (2), 402-407, 2019 | 28 | 2019 |
| A computationally efficient robust tube based MPC for linear switched systems K Hariprasad, S Bhartiya Nonlinear Analysis: Hybrid Systems 19, 60-76, 2016 | 28 | 2016 |
| Reinforcement learning based control of batch polymerisation processes V Singh, H Kodamana IFAC-PapersOnLine 53 (1), 667-672, 2020 | 25 | 2020 |
| An adaptive, interacting, cluster-based model for predicting the transmission dynamics of COVID-19 R Ravinder, S Singh, S Bishnoi, A Jan, A Sharma, H Kodamana, ... Heliyon 6 (12), 2020 | 24* | 2020 |
Biao HuangUniversity of Alberta, Canada在 ualberta.ca 的电子邮件经过验证
