| Machine learning assisted hybrid models can improve streamflow simulation in diverse catchments across the conterminous US G Konapala, S Kao, S Painter, D Lu Environmental Research Letter 15 (10), 2020 | 156 | 2020 |
| Bayesian analysis of data-worth considering model and parameter uncertainties SP Neuman, L Xue, M Ye, D Lu Advances in Water Resources 36, 75-85, 2012 | 146 | 2012 |
| An adaptive sparse‐grid high‐order stochastic collocation method for Bayesian inference in groundwater reactive transport modeling G Zhang, D Lu, M Ye, M Gunzburger, C Webster Water Resources Research 49 (10), 6871-6892, 2013 | 101 | 2013 |
| UCODE_2014, with new capabilities to define parameters unique to predictions, calculate w eights using simulated values, estimate parameters w ith SVD, evaluate uncertainty … EP Poeter, MC Hill, D Lu, CR Tiedeman, SW Mehl Integrated Groundwater Modeling Center (IGWMC), of the Colorado School of Mines, 2014 | 94 | 2014 |
| Analysis of regression confidence intervals and Bayesian credible intervals for uncertainty quantification D Lu, M Ye, MC Hill Water resources research 48 (9), 2012 | 83 | 2012 |
| Practical use of computationally frugal model analysis methods MC Hill, D Kavetski, M Clark, M Ye, M Arabi, D Lu, L Foglia, S Mehl Groundwater 54 (2), 159-170, 2016 | 76 | 2016 |
| Streamflow Simulation in Data-Scarce Basins Using Bayesian and Physics-Informed Machine Learning Models D Lu, G Konapala, S Painter, SC Kao, S Gangrade Journal of Hydrometeorology, 1421-1438, 2021 | 73 | 2021 |
| A Taylor Expansion-Based Adaptive Design Strategy for GlobalSurrogate Modeling With Applications in Groundwater Modeling S Mo, D Lu, X Shi, G Zhang, M Ye, J Wu, J Wu Water Resources Research 53, 10,802–10,823, 2017 | 67 | 2017 |
| Evaluating marginal likelihood with thermodynamic integration method and comparison with several other numerical methods P Liu, AS Elshall, M Ye, P Beerli, X Zeng, D Lu, Y Tao Water Resources Research 52 (2), 734-758, 2016 | 62 | 2016 |
| Seasonal changes in GPP/SIF ratios and their climatic determinants across the northern hemisphere A Chen, J Mao, D Ricciuto, L D., J Xiao, X Li, PE Thornton, AK Knapp Global Change Biology, 1-12, 2021 | 61 | 2021 |
| Effects of error covariance structure on estimation of model averaging weights and predictive performance D Lu, M Ye, PD Meyer, GP Curtis, X Shi, XF Niu, SB Yabusaki Water Resources Research 49 (9), 6029-6047, 2013 | 49 | 2013 |
| Efficient surrogate modeling methods for large-scale Earth system models based on machine learning techniques D Lu, D Ricciuto Geoscientific Model Development 12, 1791-1807, 2019 | 47 | 2019 |
| A computer program for uncertainty analysis integrating regression and Bayesian methods D Lu, M Ye, MC Hill, EP Poeter, GP Curtis Environmental Modelling & Software 60, 45-56, 2014 | 47 | 2014 |
| Time series anomaly detection in power electronics signals with recurrent and ConvLSTM autoencoders MI Radaideh, C Pappas, J Walden, D Lu, L Vidyaratne, T Britton, K Rajput, ... Digital Signal Processing 130, 103704, 2022 | 42 | 2022 |
| Multimodel Bayesian analysis of data-worth applied to unsaturated fractured tuffs D Lu, M Ye, SP Neuman, L Xue Advances in Water Resources 35, 69-82, 2012 | 41 | 2012 |
| Multimodel Bayesian analysis of data-worth applied to unsaturated fractured tuffs M Ye, D Lu, SP Neuman, L Xue International Conference on Groundwater: Our Source of Security in an …, 2011 | 41 | 2011 |
| Bayesian calibration of terrestrial ecosystem models: a study of advanced Markov chain Monte Carlo Methods D Lu, D Ricciuto, A Walker, C Safta, W Munger Biogeosciences 14, 4295-4314, 2017 | 40 | 2017 |
| An improved multilevel Monte Carlo method for estimating probability distribution functions in stochastic oil reservoir simulations D Lu, G Zhang, C Webster, C Barbier Water Resources Research 52, 9642-9660, 2016 | 39 | 2016 |
| Dependence of Bayesian model selection criteria and Fisher information matrix on sample size D Lu, M Ye, SP Neuman Mathematical Geosciences 43, 971-993, 2011 | 37 | 2011 |
| Comment on ‘‘Inverse groundwater modeling for hydraulic conductivity estimation using Bayesian model averaging and variance window’’by Frank T.-C. Tsai and Xiaobao Li M Ye, D Lu, SP Neuman, PD Meyer Water Resour. Res 46 (2), 2802, 2010 | 37 | 2010 |
Ming YeFlorida State UniversityDirección de correo verificada de fsu.edu