| Vol-2964 urn:nbn:de:0074-2964-3 Copyright © 2021 for the individual papers by the papers' authors. Copyright © 2021 for the volume as a collection by its editors. This volume and its papers are published under the Creative Commons License Attribution 4.0 International (CC BY 4.0).

# AAAI-MLPS 2021
Combining Artificial Intelligence and Machine Learning with Physical Sciences

### Proceedings of the AAAI 2021 Spring Symposium on Combining Artificial Intelligence and Machine Learning with Physical Sciences

### Stanford, CA, USA, March 22nd to 24th, 2021.

Edited by

### Jonghyun Lee, University of Hawaii at Manoa, USA
Eric F. Darve, Stanford University, USA
Peter K. Kitanidis, Stanford University, USA
Michael W. Mahoney, University of California, Berkeley, USA
Anuj Karpatne, Virginia Tech, 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

### Extended Abstracts and Short Papers

- A Block Coordinate Descent Optimizer for Classification Problems Exploiting Convexity
  Ravi Patel, Nathaniel Trask, Mamikon Gulian, Eric Cyr
- A Deep Learning Algorithm for Piecewise Linear Interface Construction (PLIC)
  Mohammadmehdi Ataei, Erfan Pirmorad, Franco Costa, Sejin Han, Chul Park, Markus Bussmann
- Accelerating Simulation of Stiff Nonlinear Systems using Continuous-Time Echo State Networks
  Ranjan Anantharaman, Yingbo Ma, Shashi Gowda, Chris Laughman, Viral Shah, Alan Edelman, Chris Rackauckas
- ADCME MPI: Distributed Machine Learning for Computational Engineering
  Kailai Xu, Eric Darve
- AI Research Associate for Early-Stage Scientific Discovery
  Morad Behandish, John Maxwell, Johan de Kleer
- Applications of Koopman Mode Analysis to Neural Networks
  Ryan Mohr, Maria Fonoberova, Iva Manojlovic, Aleksandr Andrejcuk, Zlatko Drmac, Yannis Kevrekidis, Igor Mezic
- Bayesian-Inference-based Inverse Estimation of Small Angle Scattering
  Akinori Asahara, Hidekazu Morita, Kanta Ono, Masao Yano, Chiharu Mitsumata, Tetsuya Shoji, Kotaro Saito
- Combining Programmable Potentials and Neural Networks for Materials Problems
  Ryan Mohr, Allan Avila, Soham Gosh, Ananta Bhattarai, Muqiao Yang, Xintian Feng, Martin Head-Gordon, Ruslan Salakhutdinov, Maria Fonoberova, Igor Mezic
- Convolutional LSTM for Planetary Boundary Layer Height (PBLH) Prediction
  Dorsa Ziaei, Jennifer Sleeman, Milton Halem, Vanessa Caicedo, Ruben Delgado, Belay Demoz
- Data Driven Physics Constrained Perturbations for Turbulence Model Uncertainty Estimation
  Jan Felix Heyse, Aashwin Ananda Mishra, Gianluca Iaccarino
- Data-based Discovery of Governing Equations
  Waad Subber, Piyush Pandita, Sayan Ghosh, Genghis Khan, Liping Wang, Roger Ghanem
- Data-driven Learning of Nonlocal Models: from high-fidelity simulations to constitutive laws
  Huaiqian You, Yue Yu, Stewart Silling, Marta D'Elia
- Discovery of Physics and Characterization of Microstructure from Data with Bayesian Hidden Physics Models
  Steven Atkinson, Yiming Zhang, Liping Wang
- Extended Physics-informed Neural Networks (XPINNs): A Generalized Space-Time Domain Decomposition based Deep Learning Framework for Nonlinear Partial Differential Equations
  Ameya Jagtap, George Karniadakis
- Generalized Physics-Informed Machine Learning for Transient Physical Systems
  Rishith Ellath Meethal, Leela Sai Prabhat Reddy Kondamadugula, Mohamed Khalil, Birgit Obst, Roland Wüchner
- Graph Networks with Physics-aware Knowledge Informed in Latent Space
  Sungyong Seo, Yan Liu
- Greedy Fiedler Spectral Partitioning for Data-driven Discrete Exterior Calculus
  Andy Huang, Nathaniel Trask, Christopher Brissette, Xiaozhe Hu
- Learning Dynamical Systems across Environments
  Yuan Yin, Ibrahim Ayed, Emmanuel de Bézenac, Patrick Gallinari
- Learning High-Dimensional Hilbert-Valued Functions With Deep Neural Networks From Limited Data
  Ben Adcock, Simone Brugiapaglia, Nick Dexter, Sebastian Moraga
- Learning Physics-guided Neural Networks with Competing Physics Loss: A Summary of Results in Solving Eigenvalue Problems
  Mohannad Elhamod, Jie Bu, Christopher Singh, Matthew Redell, Abantika Ghosh, Viktor Podolskiy, Wei-Cheng Lee, Anuj Karpatne
- Learning Potentials of Quantum Systems using Deep Neural Networks
  Arijit Sehanobish, Hector Corzo, Onur Kara, David van Dijk
- LSTMs for Inferring Planetary Boundary Layer Height (PBLH)
  Zeenat Ali, Dorsa Ziaei, Jennifer Sleeman, Zhifeng Yang, Milton Halem
- MatVAE: Independently Trained Nested Variational Autoencoder for Generating Chemical Structural Formula
  Yoshihiro Osakabe, Akinori Asahara
- Modeling Physically-Consistent, Chaotic Spatiotemporal Dynamics with Echo State Networks
  Alisha Sharma, Kaiyan Shi, Yiling Qiao, Matthew Ziemann
- Neural Ordinary Differential Equations for Data-Driven Reduced Order Modeling of Environmental Hydrodynamics
  Sourav Dutta, Peter Rivera-Casillas, Matthew Farthing
- Neural Process for Black-box Model Optimization Under Bayesian Framework
  Zhongkai Shangguan, Lei Lin, Wencheng Wu, Beilei Xu
- Partition of Unity Networks: Deep HP-Approximation
  Kookjin Lee, Nathaniel Trask, Ravi Patel, Mamikon Gulian, Eric Cyr
- Physics-Informed Machine Learning Simulator for Wildfire Propagation
  Luca Bottero, Francesco Calisto, Giovanni Graziano, Valerio Pagliarino, Martina Scauda, Sara Tiengo, Simone Azeglio
- Physics-informed Neural Networks for Solving Coupled Flow and Transport System
  Sanghyun Lee, Teeratorn Kadeethum
- Reduced-order Model for Fluid Flows via Neural Ordinary Differential Equations
  Carlos Jose Gonzalez Rojas, Andreas Dengel, Mateus Dias Ribeiro
- Self-Adaptive Physics-Informed Neural Networks using a Soft Attention Mechanism
  Levi McClenny, Ulisses Braga-Neto
- Sparsely Constrained Neural Networks for Model Discovery of PDEs
  Gert-Jan Both, Gijs Vermarien, Remy Kusters
- Surrogate Modeling for Physical Systems with Preserved Properties and Adjustable Tradeoffs
  Randi Wang, Morad Behandish
- TextureVAE: Learning Interpretable Representations of Material Microstructures Using Variational Autoencoders
  Avadhut Sardeshmukh, Sreedhar Reddy, Bp Gautham, Pushpak Bhattacharyya
- Toward Geometrical Robustness with Hybrid Deep Learning and Differential Invariants Theory
  Pierre-Yves Lagrave, Mathieu Riou
- Validation of Deep Convolutional Generative Adversarial Networks for High Energy Physics Calorimeter Simulations
  Florian Rehm, Sofia Vallecorsa, Kerstin Borras, Dirk Krücker
- Variational Autoencoders for Learning Nonlinear Dynamics of PDEs and Reductions
  Ryan Lopez, Paul Atzberger

### Extended Abstracts

- Accelerating High-fidelity Combustion Simulations with Classification Algorithms
  Wai Tong Chung, Aashwin Mishra, Nikolaos Perakis, Matthias Ihme
- Accurate Machine Learning-based Diagnostic with Quantified Uncertainties
  Adi Hanuka, Owen Convery
- Deep Autoencoders for Nonlinear Physics-Constrained Data-Driven Computational Framework with Application to Biological Tissue Modeling
  Xiaolong He, Qizhi He, Jiun-Shyan Chen
- Deep Learning-based Fast Solver of the Shallow Water Equations
  Mojtaba Forghani, Yizhou Qian, Jonghyun Lee, Matthew Farthing, Tyler Hesser, Peter Kitanidis, Eric Darve
- Graph-Informed Neural Networks
  Søren Taverniers, Eric J. Hall, Markos A. Katsoulakis, Daniel M. Tartakovsky
- Learning the Principle of Least Action with Reinforcement Learning
  Zehao Jin, Joshua Yao-Yu Lin, Siao-Fong Li
- Machine Learning Application for Permeability Estimation of Three-Dimensional Rock Images
  Hongkyu Yoon, Darryl Melander, Stephen Verzi
- Model Reduction for the Material Point Method on Nonlinear Manifolds Using Deep Learning
  Peter Yichen Chen, Maurizio Chiaramonte, Eitan Grinspun, Kevin Carlberg
- Physics Informed Deep Learning for Well Test Analysis
  Balakrishna D R, Kamalkumar Rathinasamy, Avijit Das, Keerthi Ashwin, Vani Sivasankaran, Soundararajan Rajendran
- Stiff-PINN: Physics-Informed Neural Network for Stiff Chemical Kinetics
  Weiqi Ji, Weilun Qiu, Zhiyu Shi, Shaowu Pan, Sili Deng

2021-09-11: submitted by Jonghyun Lee, metadata incl. bibliographic data published under Creative Commons CC0
2021-10-03: published on CEUR-WS.org |valid HTML5|