Integration of Neural Network-Based Symbolic Regression in Deep Learning for Scientific Discovery
Name
Integration_of_Neural_Network-Based_Symbolic_Regression_in_Deep_Learning_for_Scientific_Discovery.pdf
Description
Published version
Size
2.24 MB
Format
Adobe PDF
Checksum (MD5)
8c849b35b83c6c44c4ca05f4ab9d1c20
Author(s)
Kim, Samuel
Lu, Peter Y
Mukherjee, Srijon
Gilbert, Michael
Jing, Li
Ceperic, Vladimir
Soljacic, Marin
Date Issued
2020
Journal
IEEE Transactions on Neural Networks
Publisher
Institute of Electrical and Electronics Engineers (IEEE)
Citation
Kim, Samuel, Lu, Peter Y, Mukherjee, Srijon, Gilbert, Michael, Jing, Li et al. 2020. "Integration of Neural Network-Based Symbolic Regression in Deep Learning for Scientific Discovery." IEEE Transactions on Neural Networks, 32 (9).
Version
Final published version
Abstract
Symbolic regression is a powerful technique to discover analytic equations that describe data, which can lead to explainable models and the ability to predict unseen data. In contrast, neural networks have achieved amazing levels of accuracy on image recognition and natural language processing tasks, but they are often seen as black-box models that are difficult to interpret and typically extrapolate poorly. In this article, we use a neural network-based architecture for symbolic regression called the equation learner (EQL) network and integrate it with other deep learning architectures such that the whole system can be trained end-to-end through backpropagation. To demonstrate the power of such systems, we study their performance on several substantially different tasks. First, we show that the neural network can perform symbolic regression and learn the form of several functions. Next, we present an MNIST arithmetic task where a convolutional network extracts the digits. Finally, we demonstrate the prediction of dynamical systems where an unknown parameter is extracted through an encoder. We find that the EQL-based architecture can extrapolate quite well outside of the training data set compared with a standard neural network-based architecture, paving the way for deep learning to be applied in scientific exploration and discovery.
MIT Department
Massachusetts Institute of Technology. Department of Electrical Engineering and Computer Science
Massachusetts Institute of Technology. Department of Physics
Terms of Use
Creative Commons Attribution 4.0 International License
Persistent DSpace Link
DOI of Published Version
https://doi.org/10.1109/TNNLS.2020.3017010
