CEUR-WS.org/Vol-2587 - AAAI 2020 Spring Symposium on Combining Artificial Intelligence and Machine Learning with Physical Sciences (AAAI-MLPS)
Vol-2587
urn:nbn:de:0074-2587-2
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the individual papers by the papers' authors.
Copyright ©
2020
for the volume
as a collection by its editors.
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AAAI-MLPS 2020
Combining Artificial Intelligence and Machine Learning with Physical Sciences
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
Edited by
Jonghyun Lee
, University of Hawaii at Manoa, USA
Eric F. Darve
, Stanford University, USA
Peter K. Kitanidis
, Stanford University, USA
Matthew W. Farthing
, U.S. Army Engineer Research and Development Center, USA
Tyler Hesser
, U.S. Army Engineer Research and Development Center, USA
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
2020-03-31: submitted by Jonghyun Lee, metadata incl. bibliographic data published under
Creative Commons CC0
2020-04-01
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