Fan, M., Liu, Y., Lu, D., Wang, H., and Zhang, G. "A novel conditional generative model for efficient ensemble forecasts of state variables in large-scale geological carbon storage." Journal of Hydrology (2024). DOI: 10.1016/j.jhydrol.2024.132323

Lu, D., Liu, Y., Zhang, Z., Bao, F., and Zhang, G. "A diffusion-based uncertainty quantification method to advance E3SM land model calibration." Journal of Geophysical Research: Machine Learning and Computation (2024). DOI: 10.1029/2024JH000234

Tayal, K., Renganathan, A., and Lu, D. "Improving streamflow predictions across CONUS by integrating advanced machine learning models and diverse data." Environmental Research Letters (2024). DOI: 10.1088/1748-9326/ad6fb7

Gao, B., Coon, E., Thornton, P., and Lu, D. "Improving the estimation of atmospheric vapor pressure using interpretable LSTM." Agricultural and Forest Meteorology (2024). DOI: 10.1016/j.agrformet.2024.109907

Jamil, A., Rucker, D., Lu, D., Cao, H., Brooks, S., and Carroll, K. "Comparison of machine learning and electrical resistivity arrays to inverse modeling for locating and characterizing subsurface targets." Journal of Applied Geophysics (2024). DOI: 10.1016/j.jappgeo.2024.105493

Goldberg, S., Schram, M., Rajput, K., Britton, T., Pappas, C., Lu, D., Walden, J., Radaideh, M., Cousineau, S., and Harare, S. "Distance-preserving machine learning for uncertainty-aware accelerator capacitance predictions." Machine Learning: Science and Technology (2024). DOI: 10.1088/2632-2153/ad7cbf

Kim, C., Chae, K., Smith, M., Bardayan, D., Bruce, C., deBoer, R., Lu, D., and Odell, D. "Probabilistic neural networks for improved analyses with phenomenological models." Physical Review C (2024). DOI: 10.1103/PhysRevC.110.054609

Smith, M., and Lu, D. "Machine learning opportunities for nucleosynthesis studies." Frontiers in Astronomy and Space Science (2024). DOI: 10.3389/fspas.2024.1494439

Fan, M., Wang, H., Zhang, J., Hosseini, S., and Lu, D. "Advancing spatiotemporal forecasts of CO2 plume migration using deep learning networks with transfer learning and interpretation analysis." International Journal of Greenhouse Gas Control (2024). DOI: 10.1016/j.ijggc.2024.104061

Fan, M., Liu, S., and Lu, D. "Advancing subseasonal reservoir inflow forecasts using an explainable machine learning method." Journal of Hydrology: Regional Studies (2023). DOI: 10.1016/j.ejrh.2023.101584

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 Modeling and Software (2023). DOI: 10.1016/j.envsoft.2023.105849

Lu, D., Yang, T., and Liu, X. "Editorial: Data-driven machine learning for advancing hydrological and hydraulic predictability." Frontier in Water (2023). DOI: 10.3389/frwa.2023.1215966

Liu, S., Fan, M., and Lu, D. "Uncertainty quantification of machine learning-based permeability estimation from rock images." Geoenergy Science and Engineering (2023). DOI: 10.2139/ssrn.4279760

Alanazi, Y., Schram, M., Rajput, K., Goldenberg, S., Vidyaratne, T., Pappas, C., Radaideh, M., Lu, D., Ramuhalli, P., and Cousineau, S. "Multi-module based CVAE to predict HVCM faults in the SNS accelerator." Machine Learning with Applications (2023). DOI: 10.1016/j.mlwa.2023.100484

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

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 Resources Research (2023). DOI: 10.1029/2022WR033880

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). DOI: 10.1029/2022MS003135

Liu, S., Lu, D., Painter, S., Griffiths, N., and Pierce, E. "Uncertainty quantification of machine learning models to improve streamflow prediction in changing climate conditions." Frontiers in Water (2023). DOI: 10.3389/frwa.2023.1150126

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 (2023). DOI: 10.3389/frwa.2023.1112970

Chen, X., Serrano, M., Hernandez, R., Lu, D., Sokolov, M., Gonzalez De Vicente, S., 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 (2023). DOI: 10.1016/j.nme.2023.101393

Hao, Y., Feng, E., Lu, D., Zimmer, L., Morgan, Z., Chakoumakos, B., Zhang, G., and Cao, H. "Machine learning-assisted automation of single-crystal neutron diffraction." Journal of Applied Crystallography (2023). DOI: 10.1107/S1600576723001516

Bhanja, S., Coon, E., Lu, D., and Painter, S. "Evaluation of distributed process-based hydrologic model performance in diverse catchments using only a priori information." Journal of Hydrology (2023). DOI: 10.1016/j.jhydrol.2023.129176

Fan, M., Lu, D., and Liu, S. "A deep learning-based forecasting of CO2 plume migration." Geoenergy Science and Engineering (2023). DOI: 10.1016/j.geoen.2022.211363

Fan, M., Lu, D., Rastogi, D., and Pierce, E. "A spatiotemporal-aware climate model ensembling method for improving precipitation predictability." Journal of Machine Learning in Modeling and Simulation (2022). DOI: 10.1615/JMachLearnModelComput.2022046715

Lu, D., Painter, S., Azzolina, N., 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 (2022). DOI: 10.3389/fenrg.2021.752185

Gangrade, S., Lu, D., Kao, S., Painter, S., and Coon, E. "Machine learning assisted reservoir operation model for long-term water management simulation." Journal of the American Water Resources Association (2022). DOI: 10.1111/1752-1688.13060

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 (2021). DOI: 10.1175/JHM-D-20-0082.1

Radaideh, M., Pappas, C., Walden, C., Lu, D., Vidyaratne, L., Britton, T., Rajput, K., Schram, M., and Cousineau, S. "Time series anomaly detection in power electronics signals with recurrent and convlstm autoencoders." Digital Signal Processing (2022). DOI: 10.2139/ssrn.4069225

Huang, X., Lu, D., Ricciuto, D., Hanson, P., Richardson, A., Lu, X., Weng, E., Nie, S., Jiang, L., Hou, E., Steinmacher, I., and Luo, Y. "A model-independent data assimilation (MIDA) module and its applications in ecology." Geoscientific Model Development (2021). DOI: 10.5194/gmd-14-5217-2021

Chen, A., Mao, J., Ricciuto, D., Lu, D., Xiao, J., Li, X., Thornton, P., and Knapp, A. "Seasonal changes in GPP/SIF ratios and their climatic determinants across the northern hemisphere." Global Change Biology (2021). DOI: 10.1111/gcb.15775

Zhang, P., Liu, S., Lu, D., Sankaran, R., and Zhang, G. "An out-of-distribution-aware autoencoder model for reduced chemical kinetics." Discrete and Continuous Dynamical Systems - S (2022). DOI: 10.3934/dcdss.2021138

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 (2021). DOI: 10.1111/gcb.15366

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 Letters (2020). DOI: 10.1088/1748-9326/aba927

Lu, D., and Ricciuto, D. "Efficient surrogate modeling methods for large-scale Earth system models based on machine learning techniques." Geoscientific Model Development (2019). DOI: 10.5194/gmd-12-1791-2019

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 (2019). DOI: 10.1016/j.cageo.2019.01.012

Evans, K., Kennedy, J., Lu, D., Forrester, M., Price, S., Fyke, J., Bennett, A., Hoffman, M., Tezaur, I., Zender, C., and Vizcaino, M. "Automated and extensible ice sheet model validation." Geoscientific Model Development (2019). DOI: 10.5194/gmd-12-1067-2019

Walker, A., Ye, M., Lu, D., De Kauwe, M., Gu, L., Medlyn, B., Rogers, A., and Serbin, S. "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." Geoscientific Model Development (2018). DOI: 10.5194/gmd-11-3159-2018

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 (2018). DOI: 10.1029/2017MS001134

Lu, D., Ricciuto, D., and Evans, K. "An efficient Bayesian data-worth analysis using a multilevel Monte Carlo method." Advances in Water Resources (2018). DOI: 10.1016/j.advwatres.2018.01.024

Shi, X., Finsterle, S., Zhang, K., and Lu, D. "Advances in multiphase flow and transport in the subsurface environment." Geofluids (2018). DOI: 10.1155/2018/2906326

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 (2017). DOI: 10.1002/2017WR021622

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 (2017). DOI: 10.5194/bg-14-4295-2017

Xi, M., Lu, D., Gui, G., Qi, Z., and Zhang, G. "Calibration of an agricultural-hydrological model (RZWQM2) using surrogate global optimization." Journal of Hydrology (2017). DOI: 10.1016/j.jhydrol.2016.11.051

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 (2016). DOI: 10.1002/2016WR019475

Liu, P., Elshall, A., Ye, M., Beerli, P., Zeng, X., Lu, D., and Tao, Y. "Evaluating marginal likelihood with thermodynamic integration method and comparison with several other numerical methods." Water Resources Research (2016). DOI: 10.1002/2014WR016718

Hill, M., Kavetski, D., Clark, M., Ye, M., Arabi, M., Lu, D., Foglia, L., and Mehl, S. "Practical use of computationally frugal model analysis methods." Groundwater (2015). DOI: 10.1111/gwat.12330

Lu, D., Ye, M., and Curtis, G. "Maximum likelihood Bayesian model averaging and its predictive analysis for groundwater reactive transport models." Journal of Hydrology (2015). DOI: 10.1016/j.jhydrol.2015.07.029

Lu, D., Ye, M., Hill, M., Poeter, E., and Curtis, G. "A computer program for uncertainty analysis integrating regression and Bayesian methods." Environmental Modeling & Software (2014). DOI: 10.1016/j.envsoft.2014.06.002

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 (2013). DOI: 10.1002/wrcr.20467

Lu, D., Ye, M., Meyer, P., Curtis, G., Shi, X., Niu, X., and Yabusaki, S. "Effects of error covariance structure on estimation of model averaging weights and predictive performance." Water Resources Research (2013). DOI: 10.1002/wrcr.20441

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

Lu, D., Hill, M., and Ye, M. "Analysis of regression confidence intervals and Bayesian credible intervals for uncertainty quantification." Water Resources Research (2012). DOI: 10.1029/2011WR011289

Lu, D., Ye, M., Neuman, S., and Xue, L. "Multimodel Bayesian analysis of data-worth applied to unsaturated fractured tuffs." Advances in Water Resources (2012). DOI: 10.1016/j.advwatres.2011.10.007

Neuman, S., Xue, L., Ye, M., and Lu, D. "Bayesian analysis of data-worth considering model and parameter uncertainties." Advances in Water Resources (2012). DOI: 10.1016/j.advwatres.2011.02.007

Lu, D., Ye, M., and Neuman, S. "Dependence of Bayesian model selection criteria and Fisher information matrix on sample size." Mathematical Geoscience (2011). DOI: 10.1007/s11004-011-9359-0

Ye, M., Lu, D., Neuman, S., and Meyer, P. "Comment on 'Inverse groundwater modeling for hydraulic conductivity estimation using Bayesian model averaging and variance window'." Water Resources Research (2010). DOI: 10.1029/2009WR008501

Tayal, K., Renganathan, A., and Lu, D. "Improving streamflow predictions with vision transformers." ICLR Tackling Climate Change with Machine Learning (2024). Paper Link

Tayal, K., Renganathan, A., and Lu, D. "FutureTST: When transformers meet future exogenous drivers." ICML Next Generation of Sequence Modeling Architectures (2024). Paper Link

Tayal, K., Renganathan, A., Jia, X., Kumar, V., and Lu, D. "ExoTST: Exogenous-aware temporal sequence transformer for time series prediction." IEEE International Conference on Data Mining (2024).

Liu, Y., Lu, D., Zhang, Z., Bao, F., and Zhang, G. "Advancing Earth system model calibration: A diffusion-based method." ICLR Tackling Climate Change with Machine Learning (2024). Paper Link

Singh, R., Lu, D., and Tayal, K. "Temporal transformer to advance long-term streamflow prediction." NeurIPS Tackling Climate Change with Machine Learning (2024).

Tayal, K., Renganathan, A., and Lu, D. "Planning for floods and droughts: Intro to AI-driven hydrological modeling." ICLR Tackling Climate Change with Machine Learning (2024). Paper Link

Yin, J., Liang, S., Liu, S., Bao, F., Chipilski, H., Lu, D., and Zhang, G. "A scalable real-time data assimilation framework for predicting turbulent atmosphere dynamics." International Conference on Supercomputing (SC24) (2024). DOI: 10.1109/SCW63240.2024.00009

Wang, X., Tsaris, A., Liu, S., Choi, J., Fan, M., Zhang, W., Yin, J., Ashfaq, M., Lu, D., and Balaprakash, P. "ORBIT: Oak Ridge Base Foundation Model for Earth System Predictability." International Conference on Supercomputing (SC24) (2024). DOI: 10.1109/SC41406.2024.00007

Bororwiec, K., Lu, D., Chandan, V., Chatterjee, S., Ramuhalli, P., Tipireddy, R., Halappanavar, M., and Liu, F. "Bi-fidelity weighted transfer learning for efficient heat transfer model simulation." IEEE International Conference on Machine Learning and Applications (2023). DOI: 10.1109/ICMLA58977.2023.00147

Zhao, X., Puente, B., Liu, S., Lim, S., Gurecky, W., Lu, D., Howell, M., Liu, F., Williams, W., and Ramuhalli, P. "Knowledge-informed uncertainty-aware machine learning for time series forecasting of dynamical engineered systems." Nuclear Plant Instrumentation, Control, Human-Machine Interface Technologies (2023). Paper Link

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

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

Fan, M., Zhang, L., Liu, S., Yang, T., and Lu, D. "Identifying the hydrometeorological decision factors influencing reservoir releases over the upper Colorado region." International Conference in Data Mining (2022). DOI: 10.1109/ICDMW58026.2022.00143

Liu, S., Lu, D., Ricciuto, D., and Walker, A. "Improving net ecosystem CO2 flux prediction using memory-based interpretable machine learning." IEEE International Conference in Data Mining (2022). DOI: 10.1109/ICDMW58026.2022.00145

Radaideh, M., Pappas, C., Lu, D., Walden, J., Cousineau, S., Britton, T., Rajput, K., Vidyaratne, L., and Schram, M. "Progress on machine learning for the SNS high voltage converter modulators." North American Particle Accelerator Conference (2022). DOI: 10.18429/JACoW-NAPAC2022-WEPA38

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

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

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

Lu, D., Pierce, E., Kao, S., Li, L., and Rempe, D. "Machine learning-enabled model-data integration for predicting subsurface water storage." NeurIPS Tackling Climate Change with Machine Learning (2021). Workshop Link

Lu, D., Painter, S., and Azzolina, N. "Accurate and timely forecasts of geologic carbon storage using machine learning methods." NeurIPS Tackling Climate Change with Machine Learning (2021). Workshop Link

Pappas, C., Lu, D., Schram, M., and Vrabie, D. "Machine learning for improved availability of the SNS klystron high voltage converter modulators." International Particle Accelerator Conference (2021). DOI: 10.18429/JACoW-IPAC2021-THPAB252

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

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

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

Lu, D., and Ricciuto, D. "Efficient distance-based global sensitivity analysis for terrestrial ecosystem modeling." IEEE International Conference on Data Mining (2020). DOI: 10.1109/ICDMW51313.2020.00052

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

Lu, D., and Ricciuto, D. "Learning-based inversion-free model-data integration to advance ecosystem model prediction." IEEE International Conference on Data Mining (2019). DOI: 10.1109/ICDMW.2019.00049

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Zhang, G., Lu, D., Ye, M., Gunzburger, M., and Webster, C. "An efficient surrogate modeling approach in Bayesian uncertainty analysis." International Conference of Numerical Analysis and Applied Mathematics (2013). DOI: 10.1063/1.4825643

Ye, M., Lu, D., Neuman, S., and Xue, L. "Multimodel Bayesian analysis of data-worth applied to unsaturated fractured tuffs." International Conference on Groundwater (2011).

Lu, D., Hill, M., and Ye, M. "Analysis of regression and Bayesian predictive uncertainty measures." MODFLOW and More Conference (2011).

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

Ye, M., Lu, D., Miller, G., Curtis, G., Meyer, P., and Yabusaki, S. "Assessment of predictive uncertainty in coupled groundwater reactive transport modeling." Goldschmidt Conference: Earth, Energy and Environment (2010).