Enforcing constraints for time series prediction in supervised, unsupervised and reinforcement learning

May 17, 2019
Panos Stinis

* 30 pages, 5 figures 

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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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Doing the impossible: Why neural networks can be trained at all

May 28, 2018
Nathan O. Hodas, Panos Stinis

* The material is based on a poster from the 15th Neural Computation and Psychology Workshop "Contemporary Neural Network Models: Machine Learning, Artificial Intelligence, and Cognition" August 8-9, 2016, Drexel University, Philadelphia, PA, USA 

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Enforcing constraints for interpolation and extrapolation in Generative Adversarial Networks

Mar 22, 2018
Panos Stinis, Tobias Hagge, Alexandre M. Tartakovsky, Enoch Yeung

* 29 pages 

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Solving differential equations with unknown constitutive relations as recurrent neural networks

Oct 06, 2017
Tobias Hagge, Panos Stinis, Enoch Yeung, Alexandre M. Tartakovsky

* 19 pages, 8 figures 

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