@inproceedings{93b688c4c49c4260a44db20534e20900,
title = "Symbolic Regression for Interpretable Scientific Discovery",
abstract = "Symbolic Regression (SR) is emerging as a promising machine learning tool to directly learn succinct, mathematical and interpretable expressions directly from data. The combination of SR with deep learning (e.g. Graph Neural Network and Autoencoders) provides a powerful toolkit for scientists to push the frontiers of scientific discovery in a data-driven manner. We briefly overview SR, autoencoders and GNN and highlight examples where they have been used to rediscover known physical phenomenon directly from data.",
keywords = "Autoencoders, Graph neural networks, Model discovery, Symbolic regression",
author = "Nour Makke and Sadeghi, {Mohammad Amin} and Sanjay Chawla",
note = "Publisher Copyright: {\textcopyright} 2022, Springer Nature Switzerland AG.; 9th International Conference on Big Data Analytics, BDA 2021 ; Conference date: 07-12-2021 Through 09-12-2021",
year = "2022",
doi = "10.1007/978-3-030-96600-3_3",
language = "English",
isbn = "9783030965990",
series = "Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)",
publisher = "Springer Science and Business Media Deutschland GmbH",
pages = "26--40",
editor = "Shelly Sachdeva and Yutaka Watanobe and Subhash Bhalla",
booktitle = "Big-Data-Analytics in Astronomy, Science, and Engineering - 9th International Conference on Big Data Analytics, BDA 2021, Proceedings",
address = "Germany",
}