H052
Scientific Machine Learning for Flow, Transport, and Coupled Processes Across Temporal and Spatial Scales II

Tuesday, 8 December 2020: 19:00-20:00
Virtual
Primary Convener:  Hongkyu Yoon, Sandia National Laboratories, Department of Geomechanics, Albuquerque, NM, United States
Convener:  Diana Holford Bacon, Pacific Northwest National Laboratory, Richland, WA, United States
Primary Liaison:  Diana Holford Bacon, Pacific Northwest National Laboratory, Richland, WA, United States
Chairs:  Zhangshuan Hou, Pacific Northwest National Laboratory, Richland, WA, United States and Diana Holford Bacon, Pacific Northwest National Laboratory, Richland, WA, United States
OSPA Liaison:  Jonghyun Harry Lee, University of Hawai‘i at Mānoa, Civil and Environmental Engineering, Honolulu, HI, United States
19:00
H052-01
A Deep Reinforcement Learning Approach for Managing Carbon Storage Reservoirs (Invited) (664444)
Alexander Y Sun, University of Texas at Austin, Austin, TX, United States
19:04
H052-02
Using Machine Learning Techniques to Optimize Subsurface Hydrologic Data Collection (Invited) (667766)
Ty P.A. Ferre, University of Arizona, Department of Hydrology and Atmospheric Sciences, Tucson, AZ, United States and Mohammad A. Moghaddam, The University of Arizona, Department of Hydrology and Atmospheric Sciences, Tucson, AZ, United States
19:08
H052-03
Fast Marching Method: A New Paradigm for Rapid Modeling of Subsurface Flow and Transport (Invited) (667152)
Akhil Datta-Gupta, Texas A&M Univ, College Station, TX, United States
19:12
H052-04
Machine learning for the assessment of socio-economic impacts of geophysical hazards (Invited) (667737)
Roger Ghanem, University of Southern California, Los Angeles, CA, United States, Kelly K Rose, National Energy Technology Laboratory, Albany, OR, United States and Ruda Zhang, Statistical and Applied Mathematical Sciences Institute, Research Triangle Park, United States
19:16
H052-06
Machine Learning techniques with X-ray microcomputed tomography to segment mineral phases in a Marcellus and a Mancos shale. (712540)
Parisa Asadi, PhD Student Auburn University, civil and environmental engineering, Auburn, AL, United States and Lauren E Beckingham, Assistant Professor Auburn University, Civil and Environmental Engineering, Auburn, AL, United States
19:20
H052-07
A Survey on Physics-Informed Neural Networks for Shallow Water Problems (740656)
Peter Gabriel Rivera Casillas, US Army Engineer Research and Development Center, Information Technology Laboratory, Vicksburg, MS, United States, Matthew Farthing, US Army Engineer Research and Development Center, Coastal and Hydraulics Laboratory, Vicksburg, MS, United States, Daniel Martinez-Gonzalez, Science and Technology Corporation, Moffett Field, Mountain View, CA, United States and Wesley Brewer, US Army Engineer Research and Development Center, DoD HPCMP PET/GDIT, Vicksburg, MS, United States
19:24
H052-08
Latent-Space Inversion (LSI) for Subsurface Flow Model Calibration with Physics-Informed Autoencoding (753910)
Syamil Mohd Razak and Behnam Jafarpour, University of Southern California, Los Angeles, CA, United States
19:28
H052-09
Machine learning approaches for modeling spatio-temporal patterns in subsurface energy production (734982)
Katherine Cauthen1, Srideep Musuvathy2, Jamshed Kaikaus2, Stephen J Verzi1 and Hongkyu Yoon3, (1)Sandia National Laboratories, Albuquerque, NM, United States, (2)Sandia National Laboratories, Albuquerque, United States, (3)Sandia National Laboratories, Department of Geomechanics, Albuquerque, NM, United States
19:32
H052-10
Improved monitoring of dense non-aqueous phase liquid (DNAPL) source zone remediation through hydrogeophysical inversion using variational autoencoder and Ensemble Kalman filter (701085)
Xueyuan Kang1, Amalia Kokkinaki2, Christopher Power3, Xiaoqing Shi1, Peter K Kitanidis4, Jonghyun Harry Lee5 and Jichun Wu6, (1)Nanjing University, Nanjing, China, (2)University of San Francisco, Environmental Science, San Francisco, CA, United States, (3)University of Western Ontario, Civil and Environmental Engineering, London, ON, Canada, (4)Stanford University, Department of Civil and Environmental Engineering, Stanford, CA, United States, (5)University of Hawai‘i at Mānoa, Civil and Environmental Engineering, Honolulu, HI, United States, (6)Nanjing University, School of Earth Sciences and Engineering, Nanjing, China
19:36
Discussion
See more of: Hydrology
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