Harry Lee
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Publications

Journal Papers (* indicates co-first authors)

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Numerical Modeling, Simulation & Experiment

  1. M. Hazas, F. Zilotto, J. Lee, M. Rolle, and G. Chiogna, Evolution of plume geometry, dilution and reactive mixing in porous media under highly transient flow fields at the surface water-groundwater interface, Journal of Contaminant Hydrology, 258: 104243, 2023

  2. B. Okuhata, D. Thomas, H. Dulai, B. Popp, J. Lee and A. El-Kadi, Inference of young groundwater ages and modern groundwater proportions using chlorofluorocarbon and tritium/helium-3 tracers from West Hawai‘i Island, Journal of Hydrology, 609: 127755, 2022

  3. B. Okuhata, A. El-Kadi, H. Dulai, J. Lee, C. Wada, L. Bremer, K. Burnett, J. Delevaux and C. Shuler, A density-dependent multi-species model to assess groundwater flow and nutrient transport in the coastal Keauhou aquifer, Hawaii, USA, Hydrogeology Journal, 30: 231-250, 2021

  4. S. Park, J. Lee, H. Yoon and S. Shin, Microfluidic Investigation of Salinity-Induced Oil Recovery in Porous Media during Chemical Flooding, Energy & Fuels, 35(6): 4885–4892, 2021

  5. J. Regnery, D. Li, J. Lee, K. M. Smits and J. O. Sharp, Hydrogeochemical and microbiological effects of simulated recharge and drying within a 2D meso-scale aquifer, Chemosphere, 241: 125116, 2020

  6. M. Battistel, M. Muniruzzaman, F. Onses, J. Lee and M. Rolle, Reactive Fronts in Chemically Heterogeneous Porous Media: Experimental and Modeling Investigation of Pyrite Oxidation, Applied Geochemistry, 100: 77-89, 2019

  7. J. Lee, M. Rolle, and P. K. Kitanidis, Longitudinal Dispersion Coefficients for Numerical Modeling of Groundwater Solute Transport in Heterogeneous Formations, Journal of Contaminant Hydrology, 212: 41-54, 2018

  8. J. Regnery, J. Lee, Z. W. Drumheller, J. E. Drewes, T. H. Illangasekare, P. K. Kitanidis, J. E. McCray and K. M. Smits, Trace Organic Chemical Attenuation during Managed Aquifer Recharge: Insights from a Variably Saturated 2D Tank Experiment, Journal of Hydrology, 548: 641-651, 2017

  9. J. Regnery, J. Lee, P. K. Kitanidis, T. Illangasekare, J. O. Sharp, and J. E. Drewes, Integration of Managed Aquifer Recharge for Impaired Water Sources in Urban Settings - Overcoming Current Limitations and Engineering Challenges, Environmental Engineering Science, 30(8): 409-420, 2013

Inverse Modeling, Data Assimilation & Uncertainty Quantification

  1. X. Kang, A. Kokkinaki, X. Shi, J. Lee, Z. Guo, L. Ni and J. Wu, Modeling upscaled mass discharge from complex DNAPL source zones using a Bayesian Neural Network: prediction accuracy, uncertainty quantification and source zone feature importance, accepted in Water Resources Research

  2. J. Lee, K. DeVore, T. Hesser, A. S. Bak, K. Brodie, B. Bruder, and M. W. Farthing, Blending Bathymetry: Combination of image-derived parametric approximations and celerity data sets for nearshore bathymetry estimation, Coastal Engineering, 192: 104546, 2024

  3. Andreadis et al., A first look at river discharge from SWOT satellite observations, DOI:10.22541/essoar.171535687.74678746

  4. D. Patel, J. Lee, M. W. Farthing, P. K. Kitanidis, and E. F. Darve, Multi-fidelity Hamiltonian Monte Carlo, arXiv:2405.05033

  5. J. Bao, H. Yoon, J. Lee, Subsurface Characterization using Ensemble-based Approaches with Deep Generative Models, arXiv:2310.00839

  6. Y. Seo, J. Lee, A. El-Kadi, N. Grobe, Improved methodology for deep aquifer characterization using hydrogeological, self-potential, and magnetotellurics data, arXiv:2304.10083

  7. T. McKenzie, H. Dulai, J. Lee, N. T. Dimova, I. R. Santos, B. Zhang, and William Burnett, Using Deep Learning to Model the Groundwater Tracer Radon in Coastal Waters, Water Resources Research, e2022WR033870, 2023

  8. M. Forghani, Y. Qian, J. Lee, M. Farthing, T. Hesser, P. K. Kitanidis, and E. F. Darve, Variational encoder geostatistical analysis (VEGAS) with an application to large scale riverine bathymetry, Advances in Water Resources, 170: 104323, 2022

  9. X. Kang, A. Kokkinaki, X. Shi, H. Yoon, J. Lee, P. K. Kitanidis, and J. Wu, Integration of Deep Learning-Based Inversion and Upscaled Mass-Transfer Model for DNAPL Mass-Discharge Estimation and Uncertainty Assessment, Water Resources Research, 58(10): e2022WR033277, 2022

  10. D. Lee, S. Choi, J. Lee and W. Chung, Efficient seismic numerical modelling technique using the YOLOv2-based expanding domain method, Journal of Seismic Exploration, 31(5), 2022

  11. M. Lucas, R. Longman, T. Giambelluca, A. Frazier, J. Mclean, S. Cleveland, Y. Huang and J. Lee, Optimizing automated kriging to improve spatial interpolation of monthly rainfall over complex terrain, Journal of Hydrometeorology, 23(4), 2022

  12. J. Lee, Accelerated 3D Electrical Resistivity Tomography with a Scalable Jacobian-free Approach arXiv:2202.00059

  13. W. Reese, A. Saibaba and J. Lee, Bayesian Level Set Approach for Inverse Problems with Piecewise Constant Reconstructions arXiv:2111.15620

  14. D. Lee, J. Lee, C. Shin, S. Shin, and W. Chung, Elastic Full-waveform Inversion using both the Multiparametric Approximate Hessian and the Discrete Cosine Transform, Transactions on Geoscience and Remote Sensing, 60: 1-10, 2022

  15. T. Kadeethum, D. O'Malley, J. N. Fuhg, Y. Choi, J. Lee, H. S. Viswanathan, and N. Bouklas, A framework for data-driven solution and parameter estimation of PDEs using conditional generative adversarial networks, Nature Computational Science, 1: 819-829, 2021 arXiv:2105.13136

  16. S. Kim, H. Yoon and J. Lee, Fast and scalable earth texture synthesis using spatially assembled generative adversarial neural networks, Journal of Contaminant Hydrology, 243: 103867, 2021

  17. S. Kim, W. Chung and J. Lee, Acoustic full waveform inversion using Discrete Cosine Transform, Journal of Seismic Exploration, 30(4): 365-380, 2021

  18. E. Park and J. Lee, A non-Bayesian nonparametric model for characterization of basin-scale aquifers using groundwater level fluctuations, Journal of Hydrology, 602: 126710, 2021

  19. M. Forghani, Y. Qian, J. Lee, M. Farthing, T. Hesser, P. K. Kitanidis, and E. Darve, Application of deep learning to large scale riverine flow velocity estimation, Stochastic Environmental Research and Risk Assessment, 35: 1069-1088, 2021 arXiv:2012.02620

  20. X. Kang, A. Kokkinaki, P. K. Kitanidis, X. Shi, J. Lee, S. Mo and J. Wu, Hydrogeophysical Characterization of Nonstationary DNAPL Source Zones by Integrating a Convolutional Variational Autoencoder and Ensemble Smoother, Water Resources Research, 57(2): e2020WR02853, 2021

  21. Y. Qian, M. Forghani, J. Lee, M. Farthing, T. Hesser, P. K. Kitanidis, and E. Darve, Application of Deep Learning-based Interpolation Methods to Nearshore Bathymetry arXiv:2011.09707

  22. S. E. Kim, Y. Seo, J. Hwang, H. Yoon, and J. Lee, Connectivity-informed Drainage Network Generation using Deep Convolution Generative Adversarial Networks, Scientific Reports, 11: 1519, 2021 arXiv:2006.13304

  23. X. Kang, A. Kokkinaki, P. K. Kitanidis, X. Shi, A. Revil, J. Lee, A. S. Ahmed, and J. Wu, Improved characterization of DNAPL source zones via sequential hydrogeophysical inversion of hydraulic-head, self-potential and partitioning-tracer data, Water Resources Research, 56(8): e2020WR027627, 2020

  24. A. Collins, K. Brodie, A. S. Bak, T. Hesser, M. Farthing, J. Lee, and J. Long, Bathymetric Inversion and Uncertainty Estimation from Synthetic Surf-Zone Imagery with Machine Learning, Remote Sensing, 12(20): 3364, 2020 link

  25. H. Ghorbanidehno, A. Kokkinaki, J. Lee, E. Darve, Recent developments in fast and scalable inverse modeling and data assimilation methods in hydrology, Journal of Hydrology, 591: 125266, 2020

  26. H. Ghorbanidehno, J. Lee, M. Farthing, T. Hesser, E. F. Darve, and P. K. Kitanidis, Deep learning technique for fast inference of large-scale riverine bathymetry, Advances in Water Resources, 103715, 2020

  27. H. Ghorbanidehno, J. Lee, M. Farthing, T. Hesser, P. K. Kitanidis, and E. F. Darve, Novel data assimilation for nearshore bathymetry, Journal of Atmospheric and Oceanic Technology, 36(4): 699-715, 2019

  28. H. Jeong, A. Sun, J. Lee, and B. Min, A Learning-based Data-driven Forecast Approach for Predicting Future Reservoir Performance, Advanced in Water Resources, 118, 95-109, 2018

  29. J. Lee, H. Ghorbanidehno, M. Farthing, T. Hesser, E. F. Darve, and P. K. Kitanidis, Riverine bathymetry imaging with indirect observations, Water Resources Research, 54(5): 3704-3727, 2018

  30. J. Lee, A. Kokkinaki, and P. K. Kitanidis, Fast large-scale joint inversion for deep aquifer characterization using pressure and heat tracer measurements, Transport in Porous Media, 123(3): 533-543, 2018

  31. P. K. Kang*, J. Lee*, X. Fu, S. Lee, P. K. Kitanidis, and J. Ruben, Improved Characterization of Heterogeneous Permeability in Saline Aquifers from Transient Pressure Data during Freshwater Injection, Water Resources Research, 53(5): 4444-458, 2017

  32. J. Lee, H. Yoon, P. K. Kitanidis, C. J. Werth and A. J. Valocchi, Scalable Subsurface Inverse Modeling of Huge Data Sets with an Application to Tracer Concentration Breakthrough Data from Magnetic Resonance Imaging, Water Resources Research, 52(7): 5213-5231, 2016 AGU WRR Editors’ Highlight

  33. S. Fakhreddine*, J. Lee*, P. K. Kitanidis, S. Fendorf, and M. Rolle, Imaging Geochemical Heterogeneities Using Inverse Reactive Transport Modeling: an Example Relevant for Characterizing Arsenic Mobilization and Distribution, Advances in Water Resources, 88: 186-197, 2016

  34. J. Lee and P. K. Kitanidis, Large-Scale Hydraulic Tomography and Joint Inversion of Head and Tracer Data using the Principal Component Geostatistical Approach (PCGA), Water Resources Research, 50(7): 5410-5427, 2014

  35. P. K. Kitanidis and J. Lee, Principal Component Geostatistical Approach for Large-Dimensional Inverse Problem, Water Resources Research, 50(7): 5428-5443, 2014

  36. J. Lee and P. K. Kitanidis, Bayesian Inversion with Total Variation Prior for Discrete Geologic Structure Identification, Water Resources Research, 49(11): 7658-7669, 2013

  37. D. Hammami, T. Lee, T. Ouarda, and J. Lee, Predictor Selection for Downscaling GCM data with LASSO, Journal of Geophysical Research, VOL. 117, D17116, 2012

Optimization, Control & Value of Information

  1. Z. Drumheller, K. M. Smits, T. H. Illangasekare, J. Regnery, J. Lee, and P. K. Kitanidis, Optimal Decision Making Algorithm for Managed Aquifer Recharge and Recovery Operation using Near Real-Time Data: Benchtop Scale Laboratory Demonstration, Ground Water Monitoring and Remediation, 37(1): 27-41, 2017

  2. J. Lee, X. Liu, P. K. Kitanidis, U. Kim, J. Parker, A. Bloom, and R. Lyon, Cost Optimization of DNAPL Remediation at Dover Air Force Base Site, Ground Water Monitoring and Remediation, 32(2), 48-56, 2012

  3. X. Liu, J. Lee, P. K. Kitanidis, J. Parker, and U. Kim, Value of Information as a Context-Specific Measure of Uncertainty in Groundwater Remediation, Water Resources Management, 26(6), 1513-1535, 2012

  4. D. Bau and J. Lee, Multi-Objective Optimization for the Design of Groundwater Supply Systems under Uncertain Parameter Distribution, Pacific Journal of Optimization, 7(3), 407-424, 2011

Numerical Linear Algebra

  1. R. Wang, C. Chen, J. Lee, and E. Darve, PBBFMM3D: a Parallel Black-Box Fast Multipole Method for Non-oscillatory Kernels, Journal of Parallel and Distributed Computing, 154: 64-73, 2021 arXiv:1903.02153

  2. A. Saibaba, J. Lee, and P. K. Kitanidis, Randomized Algorithms for Generalized Hermitian Eigenvalue Problems with Application to Computing Karhunen-Loeve Expansion, Numerical Linear Algebra with Applications, 23(2): 314-339, 2016 arXiv:1307.6885

Books, Book Chapters & Conference Proceedings

  1. M. Forghani, Y. Qian, J. Lee, M. Farthing, T. Hesser, P. K. Kitanidis, and E. F. Darve, Machine Learning and Projection-Based Model Reduction in Hydrology and Geosciences in Knowledge-Guided Machine Learning edited by Anuj Karpatne, Ramakrishnan Kannan, Vipin Kumar, Chapman and Hall/CRC, 2022

  2. Jonghyun Lee, Eric F. Darve, Peter K. Kitanidis, Michael W. Mahoney, Anuj Karpatne, Matthew W. Farthing and Tyler Hesser, Proceedings of the AAAI 2021 Spring Symposium on Combining Artificial Intelligence and Machine Learning with Physical Sciences

  3. Jonghyun Lee, Eric F. Darve, Peter K. Kitanidis, Matthew W. Farthing and Tyler Hesser, Proceedings of the AAAI 2020 Spring Symposium on Combining Artificial Intelligence and Machine Learning with Physical Sciences

Conference Papers and Extended Abstracts

  1. D. V. Patel, J. Lee, M. W. Farthing, T. Hesser, P. K. Kitanidis, and E. F. Darve, Improved Black-box Variational Inference for High-dimensional Bayesian Inversion involving Black-box Simulator, NeurIPS 2023 workshop Deep Learning and Inverse Problem, Dec 16, 2023

  2. A. Koniges, D. Eder, J. Lee, A. Fisher, Y. Mileyko, M. Chyba, J. McKee, Y. Seo, P. Yip, T. Schwartzentruber, C. Parisuana, and S. Glenzer, A Survey of Recent Applications of the PISALE Code and PDE Framework, 17th International Conference on Advanced Engineering Computing and Applications in Sciences (ADVCOMP 2023), Sep 25-29, 2023

  3. J. Lee, T. Hesser, M. Farthing, A. S. Bak, and K. DeVore, Scalable real-time data assimilation with various data types for accurate spatiotemporal nearshore bathymetry estimation, Coastal Engineering Proceedings, 2023

  4. J. Shen, J. Lee, and H. Yoon, Estimation of Physical Coefficients for CO2 Sequestration using Deep Generative Priors based Inverse Modeling Framework, 1st workshop on Synergy of Scientific and Machine Learning Modeling (SynS & ML) in International Conference on Machine Learning (ICML), July 28, 2023

  5. J. Bao, J. Lee, and H. Yoon, Coupling Self-Attention Generative Adversarial Network and Bayesian Inversion for Carbon Storage System, 1st workshop on Synergy of Scientific and Machine Learning Modeling (SynS & ML) in International Conference on Machine Learning (ICML), July 28, 2023

  6. H. Yoon, J. Lee, K. Teeratorn, Deep Learning-Based Data Assimilation in the Latent Space for Real-Time Forecasting of Geologic Carbon Storage, 16th International Conference on Greenhouse Gas Control Technologies (GHGT-16), Oct 23-27, 2022

  7. Y. Seo, J. Lee, A. Koniges, A. Fisher, Development of the PISALE Codebase for Simulating Flow and Transport in Large-scale Coastal Aquifer, 11th International Conference on Computational Fluid Dynamics (ICCFD11), 2022

  8. D. Patel, J. Lee, M. Forghani, Matthew W. Farthing, Tyler Hesser, Peter K. Kitanidis, and Eric F. Darve, Multi-Fidelity Hamiltonian Monte Carlo Method with Deep Learning-based Surrogate, AAAI Fall 2021 Symposium, Nov 6, 2021

  9. M. Forghani, Y. Qian, J. Lee, M. Farthing, T. Hesser, P. Kitanidis and E. Darve, Deep learning-based fast solver of the shallow water equations, AAAI Spring 2021 Symposium, March 22-24, 2021

  10. Y. Qian, M. Forghani, J. Lee, M. Farthing, T. Hesser, P. Kitanidis and E. Darve, An Application of Deep Learning-based Interpolation Methods to Nearshore Bathymetry, AAAI Fall 2020 Symposium on Physics-Guided Ai To Accelerate Scientific Discovery, Nov 13-14, 2020

  11. K. L. Brodie, A. Collins, T. J. Hesser, M. W. Farthing, A. S. Bak, J. Lee, Augmenting wave-kinematics algorithms with machine learning to enable rapid littoral mapping and surf-zone state characterization from imagery, Proc. SPIE 11413, Artificial Intelligence and Machine Learning for Multi-Domain Operations Applications II, 1141313 (22 April 2020)

  12. A. Kokkinaki, J. Lee, H. Ghorbadinehno, E.F. Darve, P.K. Kitanidis, Subsurface characterization for large-scale systems: an integrated Python-based inversion tool for TOUGH2, Proceedings of the TOUGH Symposium 2018 github

  13. J. Lee, A. Kokkinaki, Y. Li, P. K. Kitanidis, Joint inversion of Pressure and Heat Tracer data using TOUGH2, Extended Abstract in Proceedings of TOUGH2 Symposium 2015 slides

Reports

Software

Presentations