AAAI-MLPS 2021
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
  

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