@inproceedings{d872036c8bec4f5da7aea43b76e4cbe0,
title = "PI-LSTM: Physics-infused long short-term memory network",
abstract = "We introduce a novel machine learning-based fusion model termed as PI-LSTM (Physics-Infused Long Short-Term Memory Networks) that integrates first principle Physics-Based Models and Long Short-Term Memory (LSTM) network. Our architecture aims at combining equation-based models with data-driven machine learning models to enable accurate predictions of complex dynamic systems. In this hybrid architecture, recurrency aids the temporal memory of the inputs and output of the partial physics model, in a way that facilitates generalization with scarce data sets. We illustrate the application of PI-LSTM on two dynamical systems namely Inverted Pendulum and Tumor Growth. Empirical results on both test problems stand witness to the effectiveness of using physics in guiding machine learning models and the superiority of the outlined hybrid model over purely data-driven models.",
keywords = "Hybrid Modeling Metrics and Standard Problems, Hybrid Models, Long Short-Term Memory, Physics-Based Model",
author = "Singh, \{Shubhendu Kumar\} and Ruoyu Yang and Amir Behjat and Rahul Rai and Souma Chowdhury and Ion Matei",
note = "Publisher Copyright: {\textcopyright} 2019 IEEE.; 18th IEEE International Conference on Machine Learning and Applications, ICMLA 2019 ; Conference date: 16-12-2019 Through 19-12-2019",
year = "2019",
month = dec,
doi = "10.1109/ICMLA.2019.00015",
language = "English",
series = "Proceedings - 18th IEEE International Conference on Machine Learning and Applications, ICMLA 2019",
publisher = "Institute of Electrical and Electronics Engineers Inc.",
pages = "34--41",
editor = "Wani, \{M. Arif\} and Khoshgoftaar, \{Taghi M.\} and Dingding Wang and Huanjing Wang and Naeem Seliya",
booktitle = "Proceedings - 18th IEEE International Conference on Machine Learning and Applications, ICMLA 2019",
address = "United States",
}