AAAI-MLPS 2020
Combining Artificial Intelligence and Machine Learning with Physical Sciences

Table of Contents

• Preface
  Jonghyun Lee, Eric F. Darve, Peter K. Kitanidis, Matthew W. Farthing, Tyler Hesser
  
• Videos
  Camera-ready presentations uploaded on Youtube
  

Papers

• A 2D Fully Convolutional Neural Network for Nearshore And Surf-Zone Bathymetry Inversion from Synthetic Imagery of Surf-Zone using the Model Celeris
  Adam Collins, Katherine L. Brodie, Spicer Bak, Tyler Hesser, Matthew W. Farthing, Douglas W. Gamble, Joseph W. Long
  
• A Weighted Sparse-Input Neural Network Technique Applied to Identify Important Features for Vortex-Induced Vibration
  Leixin Ma, Themistocles Resvanis, Kim Vandiver
  
• Deep Learning for Climate Models of the Atlantic Ocean
  Anton Nikolaev, Ingo Richter, Peter Sadowski
  
• Deep Sensing of Ocean Wave Heights with Synthetic Aperture Radar
  Brandon Quach, Yannik Glaser, Justin Stopa, Peter Sadowski
  
• Enforcing Constraints for Time Series Prediction in Supervised, Unsupervised and Reinforcement Learning
  Panos Stinis
  
• Event-Triggered Reinforcement Learning; An Application to Buildings’ Micro-Climate Control
  Ashkan Haji Hosseinloo, Munther Dahleh
  
• Finding Multiple Solutions of ODEs with Neural Networks
  Marco Di Giovanni, David Sondak, Pavlos Protopapas, Marco Brambilla
  
• Generalized Physics-Informed Learning through Language-Wide Differentiable Programming
  Chris Rackauckas, Alan Edelman, Keno Fischer, Mike Innes, Elliot Saba, Viral B. Shah, Will Tebbutt
  
• GMLS-Nets: A Machine Learning Framework for Unstructured Data
  Nathaniel Trask, Ravi Patel, Paul Atzberger, Ben Gross
  
• Physics-Informed Machine Learning for Real-time Reservoir Management
  Maruti K. 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 Spatiotemporal Deep Learning for Emulating Coupled Dynamical Systems
  Anishi Mehta, Cory Scott, Diane Oyen, Nishant Panda, Gowri Srinivasan
  

Extended Abstracts

• Continuous Representation of Molecules using Graph Variational Autoencoder
  Mohammadamin Tavakoli, Pierre Baldi
  
• Data-Driven Inverse Modeling with Incomplete Observations
  Kailai Xu, Eric Darve
  
• DeepXDE: A Deep Learning Library for Solving Differential Equations
  Lu Lu, Xuhui Meng, Zhiping Mao, George Em Karniadakis
  
• Nonlocal Physics-Informed Neural Networks - A Unified Theoretical and Computational Framework for Nonlocal Models
  Marta D'Elia, George E. Karniadakis, Guofei Pang, Michael L. Parks
  
• Permeability Prediction of Porous Media using Convolutional Neural Networks with Physical Properties
  Hongkyu Yoon, Darryl Melander, Stephen J. Verzi
  
• Surfzone Topography-informed Deep Learning Techniques to Nearshore Bathymetry with Sparse Measurements
  Yizhou Qian, Hojat Ghorbanidehno, Matthew Farthing, Ty Hesser, Peter K. Kitanidis, Eric F. Darve