H076
Scientific Machine Learning for Flow, Transport, and Coupled Processes Across Temporal and Spatial Scales III eLightning

Wednesday, 9 December 2020: 17:30-18:30
Virtual
Primary Convener:  Hongkyu Yoon, Sandia National Laboratories, Department of Geomechanics, Albuquerque, NM, United States
Conveners:  Jonghyun Harry Lee, University of Hawai‘i at Mānoa, Civil and Environmental Engineering, Honolulu, HI, United States and Diana Holford Bacon, Pacific Northwest National Laboratory, Richland, WA, United States
Primary Liaison:  Hongkyu Yoon, Sandia National Laboratories, Department of Geomechanics, Albuquerque, NM, United States
Chairs:  Hongkyu Yoon, Sandia National Laboratories, Department of Geomechanics, Albuquerque, NM, United States and Jonghyun Harry Lee, University of Hawai‘i at Mānoa, Civil and Environmental Engineering, Honolulu, HI, United States
OSPA Liaison:  Diana Holford Bacon, Pacific Northwest National Laboratory, Richland, WA, United States
17:30
H076-01
Realtime forecasting of CO2 flow using variational autoencoder with ensemble-based data assimilation (686056)
Hongkyu Yoon, Sandia National Laboratories, Department of Geomechanics, Albuquerque, NM, United States and Jonghyun Harry Lee, University of Hawai‘i at Mānoa, Civil and Environmental Engineering, Honolulu, HI, United States
17:33
H076-02
Feasibility study of rapid CO2 plume forecasting for CO2CRC Otway Project Stage 3 (772207)
Stanislav Glubokovskikh, Curtin University, Perth, WA, Australia, Shiv Meka, Curtin Institution of Computation, Curtin University, GPO Box U1987, Perth, Western Austral-ia, 6845, Australia, Perth, Australia and Roman Pevzner, CO2CRC ltd., Melbourne, VIC, Australia
17:36
H076-03
Automated Geologic Core Description via Machine Learning (722346)
Abrar Alabbad, Abdulrahman Alshuhail, Noha Lababidi and Fatimah Alsinan, Saudi Aramco, Dhahran, Saudi Arabia
17:39
H076-04
Data-driven Reduced Order Modeling for Reactive Transport in Nuclear Waste Repository Assessments (718417)
Hannah Lu, Stanford University, Energy Resources Engineering, Stanford, CA, United States, Daniel Tartakovsky, Stanford University, Stanford, United States, Dinara Ermakova, University of California Berkeley, Nuclear Engineering, Berkeley, CA, United States and Haruko M Wainwright, Lawrence Berkeley National Laboratory, Berkeley, CA, United States
17:42
H076-05
Deep neural network-based surrogate model linked with particle swarm optimization for identification of subsurface contamination sources (670667)
Aatish Anshuman and TI Eldho, Indian Institute of Technology Bombay, Department of Civil Engineering, Mumbai, India
17:45
H076-06
Semantic segmentation of rock images using deep learning methods (769935)
John Ringer, Albuquerque, New Mexico, United States and Hongkyu Yoon, Sandia National Laboratories, Department of Geomechanics, Albuquerque, NM, United States
17:48
H076-07
Machine Learning Application for Permeability Estimation of Three-Dimensional Rock Images (735104)
Darryl Melander1, Hongkyu Yoon2 and Stephen J Verzi1, (1)Sandia National Laboratories, Albuquerque, NM, United States, (2)Sandia National Laboratories, Department of Geomechanics, Albuquerque, NM, United States
17:51
H076-08
Prediction of Flow and Reactive Transport using Physics Informed Neural Networks (717617)
Vincent Liu, Carnegie Mellon University, Pittsburgh, PA, United States; Sandia National Laboratories, Geomechanics Department, Albuquerque, PA, United States and Hongkyu Yoon, Sandia National Laboratories, Department of Geomechanics, Albuquerque, NM, United States
17:54
H076-09
Prediction in Data Scarce Regions: A Novel Clustering and Classification Method for Simulation of Earth and Environmental Systems (677398)
Keighobad Jafarzadegan and Hamid Moradkhani, The University of Alabama, Center for Complex Hydrosystems Research, Tuscaloosa, AL, United States
17:57
H076-10
Deep Bayesian Techniques to Nearshore Bathymetry with Sparse Measurements (709234)
Yizhou Qian, Stanford University, Institute for Computational and Mathematical Engineering, Stanford, CA, United States, Mojtaba Forghani, Stanford University, Mechanical Engineering, Stanford, United States, Jonghyun Harry Lee, University of Hawai‘i at Mānoa, Civil and Environmental Engineering, Honolulu, HI, United States, Matthew W Farthing, US Army Corps of Engineers, Vicksburg, MS, United States, Ty Hesser, U.S. Army Engineer Research and Development Center, Coastal and Hydraulics Laboratory, Vicksburg, MS, United States, Peter K Kitanidis, Stanford University, Department of Civil and Environmental Engineering, Stanford, CA, United States and Eric F Darve, Stanford University, Mechanical Engineering, Stanford, CA, United States
18:00
H076-11
Evaluate Salmon Redd Habitats Using a Hierarchical Physics-Informed Machine Learning Framework (754985)
Huiying Ren1, Xuehang Song1, Zhangshuan Hou1, Evan Arntzen2 and Timothy D Scheibe1, (1)Pacific Northwest National Laboratory, Richland, WA, United States, (2)Pacific Northwest National Lab, Richland, WA, United States
18:03
H076-12
A New Deep Learning Method for Crop Yield Forecasting (677012)
Keyhan Gavahi, Peyman Abbaszadeh and Hamid Moradkhani, The University of Alabama, Center for Complex Hydrosystems Research, Tuscaloosa, AL, United States
18:06
H076-13
Application of Machine Learning to Forecast the Harmful Algal Blooms in Mississippi Sound (734750)
Bala Tripura Sundari Yerrapothu, University of Southern Mississippi, School of Computing Sciences & Computer Engineering, Stennis Space Center, MS, United States, Diana N Bernstein, University of Southern Mississippi, Division of Marine Science, Stennis Space Center, MS, United States and Bikramjit Banerjee, University of Southern Mississippi, School of Computing Sciences & Computer Engineering, Hattiesburg, MS, United States
18:09
H076-14
Using Deep Learning to Forecast Human-Modified Streamflow at Ungauged Sites (769627)
Eliza Hale, Alden Keefe Sampson, David Lambl and Grey Stephen Nearing, Natel Energy Inc, Upstream Tech, Alameda, CA, United States
18:12
H076-15
Electrostatic Potential Distribution Prediction Using Convolutional Neural Networks (755000)
Bernard Chang1, Javier E. Santos1, Rodolfo Victor2 and Masa Prodanovic1, (1)The University of Texas at Austin, Hildebrand Department of Petroleum and Geosystems Engineering, Austin, TX, United States, (2)PETROBRAS, Rio De Janeiro, Brazil
18:15
H076-16
Multiphysics-informed learning algorithm for vadose zone transport modeling (684115)
Michael J Friedel, Pacific Northwest National Laboratory, Richland, WA, United States
18:18
Discussion
See more of: Hydrology
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