Fan, M., Liu, S., Lu, D., Gangrade, S., and Kao, S., Explainable machine learning model for multi-step forecasting of reservoir inflow with uncertainty quantification. Environmental Modelling & Software, V170, 2023.

Lu, D., Yang, T., and Liu, X., Editorial: Data-driven machine learning for advancing hydrological and hydraulic predictability. Frontier in Water, V5,  2023.

Liu, S., Fan, M., and Lu, D., Uncertainty quantification of the convolutional neural networks on permeability estimation from micro-CT scanned sandstone and carbonate rock images, Geoenergy Science and Engineering, 212160, 2023.

Alanazi, Y., Schram, M., Rajput, K., Goldenberg, S., T., Vidyaratne, Pappas, C., Radaideh, M., Lu, D., Ramuhalli P., Cousineau, S., Multi-module based CVAE to predict HVCM faults in the SNS accelerator. Machine Learning with Applications, V13, 2023.

Wilson, A. J., Tran, H., and Lu, D., Uncertainty quantification of capacitor switching transient location using machine learning. IEEE Transactions on Power Systems, doi: 10.1109/TPWRS.2023.3286173, 2023.

Liu, S., Lu, D., Painter, S.L., Griffiths, N.A., and Pierce, E.M., Uncertainty quantification of machine learning models to improve streamflow prediction in changing climate and environmental conditions. Frontiers in Water, 2023.

Chen, X., Serrano, M., Hernandez, R., Lu, D., Sokolov, M. A., Gonzalez De Vicente S. M., and Katoh, Y., Influence of Fatigue Precracking and Specimen Size on the Master Curve Fracture Toughness Measurements of EUROFER97 and F82H Steels. Nuclear Materials and Energy. https://doi.org/10.1016/j.nme.2023.101393, 2023. 

Fan, M.,  Zhang, L., Liu, S., Yang, T., and Lu, D., Investigation of hydrometeorological influences on reservoir releases using explainable machine learning methods. Frontiers in Water, v5, doi: 10.3389/frwa.2023.1112970, 2023.

Hao, Y., Feng, E., Lu, D., Zimmer, L., Morgan, Z., Chakoumakos, B. C., Zhang, G., and Cao, H., Machine learning assisted automation of single crystal neutron diffraction. Submitted to Journal of Applied Crystallography.  https://doi.org/10.1107/S1600576723001516, 2023.

Topp, S., Barclay, J., Diaz, J., Sun, A., Jia, X., Lu, D., Sadler, J., and Appling A., Stream temperature prediction in a shifting environment: The influence of deep learning architecture, Water Res. Research.  https://doi.org/10.1029/2022WR033880, 2023.  

Chen A., Ricciuto D., Mao, J., Wang J., Lu D., and Meng F., Improving E3SM land model photosynthesis parameterization via satellite SIF, machine learning, and surrogate modeling. Journal of Advances in Modeling Earth Systems, 2023. 

Bhanja S. N., Coon, E. T., Lu, D., and Painter S. L., Evaluation of distributed process-based hydrologic model performance in diverse catchments using only a priori information, Journal of Hydrology, doi: https://doi.org/10.1016/j.jhydrol.2023.129176, 2023.

Fan, M., Lu, D., and Liu, S., A deep learning-based direct forecasting of CO2 plume migration. Geoenergy Science and Engineering, 2023, https://doi.org/10.1016/j.geoen.2022.211363. 

Fan, M., Lu, D., Rastogi, D., and Pierce, E.M., A spatiotemporal-aware climate model ensembling method for improving precipitation predictability. journal of machine learning in modeling and simulation, V3 (4), 29-55, 2022, doi: 10.1615/JMachLearnModelComput.2022046715.  

Fan, M., Zhang, L., Liu, S., Yang, T., and Lu, D., Identifying the hydrometeorological decision factors influencing reservoir releases over the upper colorado region. Proceedings of IEEE International Conference in Data Mining DMESS workshop. 

Liu, S., Lu, D., Ricciuto, D., and Walker A., Improving net ecosystem CO2 flux prediction using memory-based interpretable machine learning. Proceedings of IEEE International Conference in Data Mining DMESS workshop. 

Lu, D., Painter, S. L., Azzolina, N. A., Burton-Kelly, M., Jiang T., and Williamson C., Accurate and rapid forecasts for geologic carbon storage via learning-based inversion-free prediction, Frontiers in Energy Research, 9: 752185, 2022.

Lu, D., Ricciuto, D., and Zhang J., Invertible neural networks for E3SM land model calibration and simulation. Proceedings of ICLR AI for Earth and Space Sciences Workshop, 2022.

Lu, D., Ricciuto, D., and Liu, S., An interpretable machine learning model for advancing terrestrial ecosystem predictions. Proceedings of ICLR AI for Earth and Space Sciences Workshop, 2022.

Liu, S., Zhang, P., Lu, D., and Zhang, G., PI3NN: Out-of-distribution-aware prediction intervals from three neural networks. Proceedings of International Conference on Learning Representations (ICLR), 2022.

Tran, H., Lu, D., and Zhang, G., Exploiting the local parabolic landscapes of adversarial losses to accelerate black-box adversarial attack, European Conference on Computer Vision (ECCV), 2022.

Radaideh, M., Pappas, C., Lu, D., Walden, J., Cousineau, S., Britton, T., Rajput, K., Vidyaratne, L., Schram, M., Progress on machine learning for the SNS high voltage converter modulators, North American Particle Accelerator Conference, 2022.

Bororwiec, K., Lu, D., Chandan, V., Chatterjee, S., Ramuhalli, P., Tipireddy, R., Halappanavar, M., and Liu, F., Bi- delity weighted transfer learning for effcient heat transfer model simulation, IEEE International Conference on Machine Learning and Applications, 2022.

Fan, M., Lu, D., and Rastogi, D., Multimodel ensemble predictions of precipitation using Bayesian neural networks. Proceedings of ICLR AI for Earth and Space Sciences Workshop, 2022.

Gangrade, S., Lu, D., Kao, S., Painter, S., and Coon, E., Machine learning assisted reservoir operation model for long-term water management simulation, J. of the Amer. Water Res. Ass., https://doi.org/10.1111/1752-1688.13060, 2022.

Radaideh, M., Pappas, C., Walden, C., Lu, D., Vidyaratne, L., Britton, T., Rajput, K., Schram, M., Cousineau, S., Time series anomaly detection in power electronics signals with recurrent and convlstm autoencoders, Digital Signal Processing, 130, 103704, https://doi.org/10.1016/j.dsp.2022.103704, 2022.

Lu, D., Konapala, G., Painter, S., and Kao, S., Streamflow simulation in data-scarce basins using Bayesian and physics-informed machine learning models, Journal of Hydrometeorology, 1421-1438, 2021.

Lu, D., Pierce, E., Kao, S., Womble, D., Li, L., Rempe, D., Machine learning-enabled model-data integration for predicting subsurface water storage. Proceedings of NeurIPS Tackling Climate Change with Machine Learning workshop, 2021.

Lu, D., Painter, S., Azzolina N., and Burton-Kelly M., Accurate and timely forecasts of geologic carbon storage using machine learning methods. Proceedings of NeurIPS Tackling Climate Change with Machine Learning workshop, 2021.

Pappas, C., Lu, D., Schram, M., and Vrabie D., Machine learning for improved availability of the SNS klystron high voltage converter modulators, 12th Int. Particle Acc. Conf. doi:10.18429/JACoW-IPAC2021-THPAB252, 2021.

Zhang, P., Liu, S., Lu, D., and Zhang, G., A prediction interval method for uncertainty quantification of regression models, Proceedings of ICLR Workshop on Deep Learning for Simulation, 2021.

Tran, H., Lu, D., and Zhang, G., Boosting black-box adversarial attack via exploiting loss smoothness, Proceedings of ICLR Workshop on Security and Safety in Machine Learning Systems, 2021.

Zhang, J., Tran, H., Lu, D., and Zhang, G., Enabling long-range exploration in minimization of multimodal functions, Proceedings of Conference on Uncertainty in Artificial Intelligence (UAI), 2021.

Huang, X., Lu, D., Ricciuto, D. M., Hanson, P. J., Richardson, A. D., Lu, X., Weng, E., Nie, S., Jiang, L., Hou, E., Steinmacher, I. F., and Luo, Y., A model-independent data assimilation (MIDA) module and its applications in ecology, Geoscientific Model Development, 14(8), 5217-5238, 2021.

Chen, A., Mao, J., Ricciuto, D., Lu, D., Xiao, J., Li, X., Thornton, P. E., and Knapp, A. K., Seasonal changes in GPP/SIF ratios and their climatic determinants across the northern hemisphere, Global Change Biology, 1-12, 2021.

Zhang, P., Liu, S., Lu, D., Sankaran, R., Zhang, G., An out-of-distribution-aware autoencoder model for reduced chemical kinetics, American Institute of Mathematical Sciences Journal, Doi: 10.3934/dcdss.2021138, 2021.

Walker, A., Johnson, A., Rogers, A., Anderson, J., Bridges, R., Fisher R., Lu, D., Ricciuto, D., Serbin, S. and Ye M., Multi-hypothesis analysis of photosynthesis models reveals the unexpected influence of empirical assumptions at leaf and global scales, Global Change Biology, Doi: 10.1111/gcb.15366, 2020.

Konapala, G., Kao, S., Painter, S., and Lu, D., Machine learning assisted hybrid models can improve streamflow simulation in diverse catchments across the conterminous US, Environmental Research Letter, 15(10), 2020.

Lu, D., and Ricciuto D., E cient distance-based global sensitivity analysis for terrestrial ecosystem modeling, Proceedings of the 2020 IEEE International Conference on Data Mining Workshops, Doi: 10.1109/ICDMW51313.2020.00052, 2020.

Lu, D. , Liu S., and Ricciuto D., An efficient Bayesian method for advancing the application of deep learning in earth science. Proceedings of the 2019 IEEE International Conference on Data Mining Workshops, Doi: 10.1109/ICDMW.2019.00048. 

Lu, D. , and Ricciuto D., Learning-based inversion-free model-data integration to advance ecosystem model prediction. Proceedings of the 2019 IEEE International Conference on Data Mining Workshops, Doi: 10.1109/ICDMW.2019.00049. 

Lu, D. and Ricciuto D., Efficient surrogate modeling methods for large-scale Earth system models based on machine learning techniques, Geoscientific Model Development, 12, 1791-1807, 2019. 

Mo, S., Shi, X., Lu,  D., Ye, M., and Wu, J., An adaptive Kriging surrogate method for efficient uncertainty quantification with an application to geological carbon sequestration modeling, Computers and Geosciences, 125, 69-77, 2019. 

Evans, K., Kennedy, J., Lu, D., Forrester, M. M.,  Price, S.,  Fyke, J., Bennett, A., Hoffman, M., Tezaur, I., Zender, C., and Vizcaino, M., LIVVkit 2.1: Automated and extensible ice sheet model validation, Geoscientific Model Development, 12, 1067-1086, 2019. 

Walker A. P., Ye, M., Lu, D., De Kauwe, M. G., Gu, L., Medlyn, B. E., Rogers, A., and Serbin, S. P., The multi-assumption architecture and testbed (MAAT v1.0): R code for generating ensembles with dynamic model structure and analysis of epistemic uncertainty from multiple sources, Geosci. Model Dev., 11, 2018. 

Lu, D., Ricciuto, D., Stoyanov, M., and Gu, L., Calibration of the E3SM land model using surrogate based global optimization, Journal of Advances in Modeling Earth Systems, 10, 1337–1356, 2018. 

Lu, D., Ricciuto, D., and Evans, K., An efficient Bayesian data-worth analysis using a multilevel Monte Carlo method, Adv. in Water Resour., 113, 223–235, 2018. 

Mo, S., Lu, D., Shi, X., Zhang, G., Ye, M., Wu, J., and Wu, J., A Taylor expansion-based adaptive design strategy for global surrogate modeling with applications in groundwater modeling, Water Resources Research, 53, 10802–10823, 2017. 

Shi, X., Finsterle, S., Zhang, K., and Lu, D., Advances in multiphase flow and trans- port in the subsurface environment., Geofluids, https://doi.org/10.1155/2018/2906326, 2018. 

Lu, D., Ricciuto, D., Walker, A., Safta, C., and Munger, W., Bayesian calibration of terrestrial ecosystem models: a study of advanced Markov chain Monte Carlo methods, Biogeosciences, 14, 4295–4314, 2017. 

Xi, M., Lu, D., Gui, D., Qi, Z., and Zhang, G., Calibration of an agricultural-hydrological model (RZWQM2) using surrogate global optimization, Journal of Hydrology, 544, 456–466, 2017. 

Lu, D., Zhang, G., Webster, C., and Barbier, C., An improved multilevel Monte Carlo method for estimating probability distribution functions in stochastic oil reservoir simulations, Water Resources Research, 52, 9642-9660, 2016.

Liu, P., Ye, M., Beerli, P., Zeng, X., Lu, D., and Tao, Y., Evaluate model probability using Markov Chain Monte Carlo with thermodynamics integration, Water Resources Research, 52(2), 734-758, 2016.

Hill, M. C., Kavetski, D., Clark, M., Ye, M., Arabi, M., Lu, D., Foglia, L., and Mehl, S., Practical use of computationally frugal model analysis methods, Ground Water, 54(2), 59-170, 2016.

Lu, D., Ye, M., and Curtis, G. P., Maximum likelihood Bayesian model averaging and its predictive analysis for groundwater reactive transport models, Journal of Hydrology, 529(3), 1859–1873, 2015.

Lu, D., Ye, M., Hill, M. C., Poeter, E. P.,  and Curtis, G. P., A computer program for uncertainty analysis integrating regression and Bayesian methods, Environmental Modeling & Software, 60, 41–56, 2014.

Zhang, G., Lu, D., Ye, M., Gunzburger, M., and Webster, C., An adaptive sparse-grid high-order stochastic collocation method of Bayesian inference in groundwater reactive transport modeling, Water Resources Research, 49(10), 6871–6892, 2013.

Lu, D., Ye, M., Meyer, P. D.,  Curtis, G. P.,  Shi, X.,  Niu, X., and Yabusaki, S. B.,  Effects of error covariance structure on estimation of model averaging weights and predictive performance, Water Resources Research, 49(9), 6029–6047, 2013. 

Zhang, G., Lu, D., Ye, M., Gunzburger, M., and Webster, C., An efficient surrogate modeling approach in Bayesian uncertainty analysis, 11th International Conference of Numerical Analysis and Applied Mathematics, 1558, 898-901, 2013.

Hill, M. C., Kavetski, D., Clark, M., Ye, M., and Lu, D., Uncertainty quantification for environmental models, SIAM News, 45(9), 2012.

Lu, D., Hill, M. C., and Ye, M., Analysis of regression confidence intervals and Bayesian credible intervals for uncertainty quantification, Water Resources Research, 48(9), W09521, 2012.  (This paper was selected as Editor’s Highlight entitled new insights into faster computation of uncertainties)

Lu, D., Ye, M., Neuman, S. P., and Xue, L., Multimodel Bayesian analysis of data-worth applied to unsaturated fractured Tuffs, Advances in Water Resources, 35, 69–82, 2012.

Neuman, S. P.,  Xue, L., Ye, M., and Lu, D., Bayesian analysis of data-worth considering model and parameter uncertainties, Advances in Water Resources, 36, 75–85, 2012. (Top 10 Cited Paper in 2012-2013 of Advances in Water Resources)

Lu, D., Ye, M., and Neuman, S. P., Dependence of Bayesian model selection criteria and Fisher information matrix on sample size, Mathematical Geoscience, 43, 971–993, 2011.

Ye, M., Lu, D., Neuman, S. P., and Xue, L., Multimodel Bayesian analysis of data-worth applied to unsaturated fractured tuffs, International Conference on Groundwater: Our Source of Security in an Uncertain Future, Pretoria, South Africa, 2011.

Lu, D., Hill, M. C., and Ye, M., Analysis of regression and Bayesian predictive uncertainty measures, MODFLOW and More 2011 Conference, Golden, CO, 2011.

Neuman, S. P., Xue, L., Ye, M., and Lu, D., Multimodel assessment of the worth of data under uncertainty, Water Management Symposium, Phoenix, AZ, 2011.

Ye, M., Lu, D., G. Miller, G. P. Curtis, P. D. Meyer, and S. B. Yabusaki, Assessment of predictive uncertainty in coupled groundwater reactive transport modeling, Conference on Goldschmidt -- Earth, Energy and Environment, Knoxville, TN, 2010.

Ye, M., Lu, D., Neuman, S. P., and Meyer, P. D., Comment on ”Inverse groundwater modeling for hydraulic conductivity estimation using Bayesian model averaging and variance window” by Frank T.-C. Tsai and Xiaobao Li, Water Resources Research, 46, W02801, 2010.

C. Barbier, D. Lu, N. Collier, F. Curtis, C. Webster, and Y. Polsky, High Performance Computing Simulations for Shale Gas Formation Flow Transport and Uncertainty Quantification Analysis, ORNL Technical Report, ORNL/TM-2015/543, 2015.