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ORCIDAAAI Spring Symposium 2021 - Combining Artificial Intelligence and Machine Learning with Physical Sciences: Stanford, CA, USA
- Jonghyun Lee, Eric F. Darve, Peter K. Kitanidis, Michael W. Mahoney, Anuj Karpatne, Matthew W. Farthing, Tyler J. Hesser:
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. CEUR Workshop Proceedings 2964, CEUR-WS.org 2021 - Jonghyun Lee, Eric F. Darve, Peter K. Kitanidis, Matthew W. Farthing, Tyler J. Hesser:
Preface.
Extended Abstracts and Short Papers
- Ravi G. Patel, Nathaniel Trask, Mamikon A. Gulian, Eric C. Cyr:
A Block Coordinate Descent Optimizer for Classification Problems Exploiting Convexity. - Mohammadmehdi Ataei, Erfan Pirmorad, Franco Costa, Sejin Han, Chul B. Park, Markus Bussmann:
A Deep Learning Algorithm for Piecewise Linear Interface Construction (PLIC). - Ranjan Anantharaman, Yingbo Ma, Shashi Gowda, Chris Laughman, Viral B. Shah, Alan Edelman, Christopher Rackauckas:
Accelerating Simulation of Stiff Nonlinear Systems using Continuous-Time Echo State Networks. - Kailai Xu, Eric Darve:
ADCME MPI: Distributed Machine Learning for Computational Engineering. - Morad Behandish, John Maxwell III, Johan de Kleer:
AI Research Associate for Early-Stage Scientific Discovery. - Ryan Mohr, Maria Fonoberova, Iva Manojlovic, Aleksandr Andrejcuk, Zlatko Drmac, Yannis G. Kevrekidis, Igor Mezic:
Applications of Koopman Mode Analysis to Neural Networks. - Akinori Asahara, Hidekazu Morita, Kanta Ono, Masao Yano, Chiharu Mitsumata, Tetsuya Shoji, Kotaro Saito:
Bayesian-Inference-based Inverse Estimation of Small Angle Scattering. - Ryan Mohr, Allan M. Avila, Soham Ghosh, Ananta Bhattarai, Muqiao Yang, Xintian Feng, Martin Head-Gordon, Ruslan Salakhutdinov, Maria Fonoberova, Igor Mezic:
Combining Programmable Potentials and Neural Networks for Materials Problems. - Dorsa Ziaei, Jennifer Sleeman, Milton Halem, Vanessa Caicedo, Ruben Delgado, Belay Demoz:
Convolutional LSTM for Planetary Boundary Layer Height (PBLH) Prediction. - Jan Felix Heyse, Aashwin Ananda Mishra, Gianluca Iaccarino:
Data Driven Physics Constrained Perturbations for Turbulence Model Uncertainty Estimation. - Waad Subber, Piyush Pandita, Sayan Ghosh, Genghis Khan, Liping Wang, Roger G. Ghanem:
Data-based Discovery of Governing Equations. - Huaiqian You, Yue Yu, Stewart Silling, Marta D'Elia:
Data-driven Learning of Nonlocal Models: from high-fidelity simulations to constitutive laws. - Steven Atkinson, Yiming Zhang, Liping Wang:
Discovery of Physics and Characterization of Microstructure from Data with Bayesian Hidden Physics Models. - Ameya D. Jagtap, George E. Karniadakis:
Extended Physics-informed Neural Networks (XPINNs): A Generalized Space-Time Domain Decomposition based Deep Learning Framework for Nonlinear Partial Differential Equations. - Rishith Ellath Meethal, Leela Sai Prabhat Reddy Kondamadugula, Mohamed Khalil, Birgit Obst, Roland Wüchner:
Generalized Physics-Informed Machine Learning for Transient Physical Systems. - Sungyong Seo, Yan Liu:
Graph Networks with Physics-aware Knowledge Informed in Latent Space. - Andy Huang, Nathaniel Trask, Christopher Brissette, Xiaozhe Hu:
Greedy Fiedler Spectral Partitioning for Data-driven Discrete Exterior Calculus. - Yuan Yin, Ibrahim Ayed, Emmanuel de Bézenac, Patrick Gallinari:
Learning Dynamical Systems across Environments. - Ben Adcock, Simone Brugiapaglia, Nick C. Dexter, Sebastian Moraga:
Learning High-Dimensional Hilbert-Valued Functions With Deep Neural Networks From Limited Data. - Mohannad Elhamod, Jie Bu, Christopher Singh, Matthew Redell, Abantika Ghosh, Viktor Podolskiy, Wei-Cheng Lee, Anuj Karpatne:
Learning Physics-guided Neural Networks with Competing Physics Loss: A Summary of Results in Solving Eigenvalue Problems. - Arijit Sehanobish, Hector H. Corzo, Onur Kara, David van Dijk:
Learning Potentials of Quantum Systems using Deep Neural Networks. - Zeenat Ali, Dorsa Ziaei, Jennifer Sleeman, Zhifeng Yang, Milton Halem:
LSTMs for Inferring Planetary Boundary Layer Height (PBLH). - Yoshihiro Osakabe, Akinori Asahara:
MatVAE: Independently Trained Nested Variational Autoencoder for Generating Chemical Structural Formula. - Alisha Sharma, Kaiyan Shi, Yiling Qiao, Matthew R. Ziemann:
Modeling Physically-Consistent, Chaotic Spatiotemporal Dynamics with Echo State Networks. - Sourav Dutta, Peter Rivera-Casillas, Matthew W. Farthing:
Neural Ordinary Differential Equations for Data-Driven Reduced Order Modeling of Environmental Hydrodynamics. - Zhongkai Shangguan, Lei Lin, Wencheng Wu, Beilei Xu:
Neural Process for Black-box Model Optimization Under Bayesian Framework. - Kookjin Lee, Nathaniel Trask, Ravi G. Patel, Mamikon A. Gulian, Eric C. Cyr:
Partition of Unity Networks: Deep HP-Approximation. - Luca Bottero, Francesco Calisto, Giovanni Graziano, Valerio Pagliarino, Martina Scauda, Sara Tiengo, Simone Azeglio:
Physics-Informed Machine Learning Simulator for Wildfire Propagation. - Sanghyun Lee, Teeratorn Kadeethum:
Physics-informed Neural Networks for Solving Coupled Flow and Transport System. - Carlos Jose Gonzalez Rojas, Andreas Dengel, Mateus Dias Ribeiro:
Reduced-order Model for Fluid Flows via Neural Ordinary Differential Equations. - Levi D. McClenny, Ulisses M. Braga-Neto:
Self-Adaptive Physics-Informed Neural Networks using a Soft Attention Mechanism. - Gert-Jan Both, Gijs Vermarien, Remy Kusters:
Sparsely Constrained Neural Networks for Model Discovery of PDEs. - Randi Wang, Morad Behandish:
Surrogate Modeling for Physical Systems with Preserved Properties and Adjustable Tradeoffs. - Avadhut Sardeshmukh, Sreedhar Reddy, Gautham B. P., Pushpak Bhattacharyya:
TextureVAE: Learning Interpretable Representations of Material Microstructures Using Variational Autoencoders. - Pierre-Yves Lagrave, Mathieu Riou:
Toward Geometrical Robustness with Hybrid Deep Learning and Differential Invariants Theory. - Florian Rehm, Sofia Vallecorsa, Kerstin Borras, Dirk Krücker:
Validation of Deep Convolutional Generative Adversarial Networks for High Energy Physics Calorimeter Simulations. - Ryan Lopez, Paul J. Atzberger:
Variational Autoencoders for Learning Nonlinear Dynamics of PDEs and Reductions.
Extended Abstracts
- Wai Tong Chung, Aashwin Ananda Mishra, Nikolaos Perakis, Matthias Ihme:
Accelerating High-fidelity Combustion Simulations with Classification Algorithms. - Adi Hanuka, Owen Convery:
Accurate Machine Learning-based Diagnostic with Quantified Uncertainties. - Xiaolong He, Qizhi He, Jiun-Shyan Chen:
Deep Autoencoders for Nonlinear Physics-Constrained Data-Driven Computational Framework with Application to Biological Tissue Modeling. - Mojtaba Forghani, Yizhou Qian, Jonghyun Lee, Matthew W. Farthing, Tyler J. Hesser, Peter K. Kitanidis, Eric Darve:
Deep Learning-based Fast Solver of the Shallow Water Equations. - Søren Taverniers, Eric Joseph Hall, Markos A. Katsoulakis, Daniel M. Tartakovsky:
Graph-Informed Neural Networks. - Zehao Jin, Joshua Yao-Yu Lin, Siao-Fong Li:
Learning the Principle of Least Action with Reinforcement Learning. - Hongkyu Yoon, Darryl J. Melander, Stephen J. Verzi:
Machine Learning Application for Permeability Estimation of Three-Dimensional Rock Images. - Peter Yichen Chen, Maurizio M. Chiaramonte, Eitan Grinspun, Kevin Carlberg:
Model Reduction for the Material Point Method on Nonlinear Manifolds Using Deep Learning. - Balakrishna D. R, Kamalkumar Rathinasamy, Avijit Das, Keerthi Ashwin, Vani Sivasankaran, Soundararajan Rajendran:
Physics Informed Deep Learning for Well Test Analysis. - Weiqi Ji, Weilun Qiu, Zhiyu Shi, Shaowu Pan, Sili Deng:
Stiff-PINN: Physics-Informed Neural Network for Stiff Chemical Kinetics.
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