AAAI Spring Symposium: MLPS

Keyphrases

Publications

2021

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

volume 2964, 2021

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. AAAI Spring Symposium: MLPS 2964 (2021)

volume 2587, 2020

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. AAAI Spring Symposium: MLPS 2587 (2020)