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Differentiable modeling to unify machine learning and physical models and advance Geosciences


Jan 10, 2023
Chaopeng Shen, Alison P. Appling, Pierre Gentine, Toshiyuki Bandai, Hoshin Gupta, Alexandre Tartakovsky, Marco Baity-Jesi, Fabrizio Fenicia, Daniel Kifer, Li Li, Xiaofeng Liu, Wei Ren, Yi Zheng, Ciaran J. Harman, Martyn Clark, Matthew Farthing, Dapeng Feng, Praveen Kumar, Doaa Aboelyazeed, Farshid Rahmani, Hylke E. Beck, Tadd Bindas, Dipankar Dwivedi, Kuai Fang, Marvin Höge, Chris Rackauckas, Tirthankar Roy, Chonggang Xu, Kathryn Lawson

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Enhanced physics-constrained deep neural networks for modeling vanadium redox flow battery


Mar 03, 2022
QiZhi He, Yucheng Fu, Panos Stinis, Alexandre Tartakovsky

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Machine Learning in Heterogeneous Porous Materials


Feb 04, 2022
Marta D'Elia, Hang Deng, Cedric Fraces, Krishna Garikipati, Lori Graham-Brady, Amanda Howard, George Karniadakis, Vahid Keshavarzzadeh, Robert M. Kirby, Nathan Kutz, Chunhui Li, Xing Liu, Hannah Lu, Pania Newell, Daniel O'Malley, Masa Prodanovic, Gowri Srinivasan, Alexandre Tartakovsky, Daniel M. Tartakovsky, Hamdi Tchelepi, Bozo Vazic, Hari Viswanathan, Hongkyu Yoon, Piotr Zarzycki

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* The workshop link is: https://amerimech.mech.utah.edu 

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Physics-constrained deep neural network method for estimating parameters in a redox flow battery


Jun 21, 2021
QiZhi He, Panos Stinis, Alexandre Tartakovsky

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Highly-scalable, physics-informed GANs for learning solutions of stochastic PDEs


Oct 29, 2019
Liu Yang, Sean Treichler, Thorsten Kurth, Keno Fischer, David Barajas-Solano, Josh Romero, Valentin Churavy, Alexandre Tartakovsky, Michael Houston, Prabhat, George Karniadakis

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* 3rd Deep Learning on Supercomputers Workshop (DLS) at SC19 

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A comparative study of physics-informed neural network models for learning unknown dynamics and constitutive relations


Apr 02, 2019
Ramakrishna Tipireddy, Paris Perdikaris, Panos Stinis, Alexandre Tartakovsky

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Physics-Informed CoKriging: A Gaussian-Process-Regression-Based Multifidelity Method for Data-Model Convergence


Nov 24, 2018
Xiu Yang, David Barajas-Solano, Guzel Tartakovsky, Alexandre Tartakovsky

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Physics-Informed Kriging: A Physics-Informed Gaussian Process Regression Method for Data-Model Convergence


Sep 14, 2018
Xiu Yang, Guzel Tartakovsky, Alexandre Tartakovsky

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* Updated Figure 2(b),(c), Figure 3(c), Figure 4 and Figure 5 

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