A Neural-Guided Dynamic Symbolic Network for Exploring Mathematical Expressions from Data

• URL: http://arxiv.org/abs/2309.13705v2
• Date: Sat, 1 Jun 2024 08:54:23 GMT
• Title: A Neural-Guided Dynamic Symbolic Network for Exploring Mathematical Expressions from Data
• Authors: Wenqiang Li, Weijun Li, Lina Yu, Min Wu, Linjun Sun, Jingyi Liu, Yanjie Li, Shu Wei, Yusong Deng, Meilan Hao,
• Abstract summary: Symbolic regression is a powerful technique for discovering the underlying mathematical expressions from observed data. Recent deep generative SR methods have shown promising results, but they face difficulties in processing high-dimensional problems. We propose DySymNet, a novel neural-guided Dynamic Symbolic Network for SR.
• Score: 12.964942755481585
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Symbolic regression (SR) is a powerful technique for discovering the underlying mathematical expressions from observed data. Inspired by the success of deep learning, recent deep generative SR methods have shown promising results. However, these methods face difficulties in processing high-dimensional problems and learning constants due to the large search space, and they don't scale well to unseen problems. In this work, we propose DySymNet, a novel neural-guided Dynamic Symbolic Network for SR. Instead of searching for expressions within a large search space, we explore symbolic networks with various structures, guided by reinforcement learning, and optimize them to identify expressions that better-fitting the data. Based on extensive numerical experiments on low-dimensional public standard benchmarks and the well-known SRBench with more variables, DySymNet shows clear superiority over several representative baseline models. Open source code is available at https://github.com/AILWQ/DySymNet.

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