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ORCIDAAAI Spring Symposium 2020 - Combining Artificial Intelligence and Machine Learning with Physical Sciences: Palo Alto, CA, USA
- Jonghyun Lee, Eric F. Darve, Peter K. Kitanidis, Matthew W. Farthing, Tyler J. Hesser:
Proceedings of the AAAI 2020 Spring Symposium on Combining Artificial Intelligence and Machine Learning with Physical Sciences, Stanford, CA, USA, March 23rd - to - 25th, 2020. CEUR Workshop Proceedings 2587, CEUR-WS.org 2020 - Jonghyun Lee, Eric F. Darve, Peter K. Kitanidis, Matthew W. Farthing, Tyler J. Hesser:
Preface.
Papers
- Adam Collins, Katherine L. Brodie, Andrew Spicer Bak, Tyler J. Hesser, Matthew W. Farthing, Douglas W. Gamble, Joseph W. Long:
A 2D Fully Convolutional Neural Network for Nearshore And Surf-Zone Bathymetry Inversion from Synthetic Imagery of Surf-Zone using the Model Celeris. - Leixin Ma, Themistocles Resvanis, Kim Vandiver:
A Weighted Sparse-Input Neural Network Technique Applied to Identify Important Features for Vortex-Induced Vibration. - Anton Nikolaev, Ingo Richter, Peter Sadowski:
Deep Learning for Climate Models of the Atlantic Ocean. - Brandon Quach, Yannik Glaser, Justin Stopa, Peter J. Sadowski:
Deep Sensing of Ocean Wave Heights with Synthetic Aperture Radar. - Panos Stinis:
Enforcing Constraints for Time Series Prediction in Supervised, Unsupervised and Reinforcement Learning. - Ashkan Haji Hosseinloo, Munther A. Dahleh:
Event-Triggered Reinforcement Learning; An Application to Buildings' Micro-Climate Control. - Marco Di Giovanni, David Sondak, Pavlos Protopapas, Marco Brambilla:
Finding Multiple Solutions of ODEs with Neural Networks. - Christopher Rackauckas, Alan Edelman, Keno Fischer, Mike Innes, Elliot Saba, Viral B. Shah, Will Tebbutt:
Generalized Physics-Informed Learning through Language-Wide Differentiable Programming. - Nathaniel Trask, Ravi G. Patel, Paul J. Atzberger
, Ben J. Gross:
GMLS-Nets: A Machine Learning Framework for Unstructured Data. - Maruti Kumar Mudunuru, Daniel O'Malley, Shriram Srinivasan, Jeffrey D. Hyman, Matthew R. Sweeney, Luke Frash, Bill Carey, Michael R. Gross, Nathan J. Welch, Satish Karra, Velimir V. Vesselinov, Qinjun Kang, Hongwu Xu, Rajesh J. Pawar, Tim Carr, Liwei Li, George D. Guthrie, Hari S. Viswanathan:
Physics-Informed Machine Learning for Real-time Reservoir Management. - Anishi Mehta, Cory Braker Scott, Diane Oyen, Nishant Panda, Gowri Srinivasan:
Physics-Informed Spatiotemporal Deep Learning for Emulating Coupled Dynamical Systems.
Extended Abstracts
- Mohammadamin Tavakoli, Pierre Baldi:
Continuous Representation of Molecules using Graph Variational Autoencoder. - Kailai Xu, Eric Darve:
Data-Driven Inverse Modeling with Incomplete Observations. - Lu Lu, Xuhui Meng, Zhiping Mao, George Em Karniadakis:
DeepXDE: A Deep Learning Library for Solving Differential Equations. - Marta D'Elia, George E. Karniadakis, Guofei Pang, Michael L. Parks:
Nonlocal Physics-Informed Neural Networks - A Unified Theoretical and Computational Framework for Nonlocal Models. - Hongkyu Yoon, Darryl J. Melander, Stephen J. Verzi:
Permeability Prediction of Porous Media using Convolutional Neural Networks with Physical Properties. - Yizhou Qian, Hojat Ghorbanidehno, Matthew W. Farthing, Ty Hesser, Peter K. Kitanidis, Eric F. Darve:
Surfzone Topography-informed Deep Learning Techniques to Nearshore Bathymetry with Sparse Measurements.
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