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20172024
most citedA comparative study of physics-informed neural network models for learning unknown dynamics and constitutive relations

34 citations · 91 across the 15 of their papers we have counts for

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math.AP2019★ 4 cited

Physics-Informed Machine Learning with Conditional Karhunen-Loève Expansions

Alexandre M. Tartakovsky, David A. Barajas-Solano, Qizhi He

We present a new physics-informed machine learning approach for the inversion of PDE models with heterogeneous parameters. In our approach, the space-dependent partially-observed p…

math.AP2018

Learning Parameters and Constitutive Relationships with Physics Informed Deep Neural Networks

Alexandre M. Tartakovsky, Carlos Ortiz Marrero, Paris Perdikaris +2

We present a physics informed deep neural network (DNN) method for estimating parameters and unknown physics (constitutive relationships) in partial differential equation (PDE) mod…