CaMo: Capturing the modularity by end-to-end models for Symbolic Regression
Modularity is a ubiquitous principle that permeates various aspects of nature, society, and
human endeavors, from biological systems to organizational structures and beyond. In the …
human endeavors, from biological systems to organizational structures and beyond. In the …
A neural-guided dynamic symbolic network for exploring mathematical expressions from data
Symbolic regression (SR) is a powerful technique for discovering the underlying
mathematical expressions from observed data. Inspired by the success of deep learning …
mathematical expressions from observed data. Inspired by the success of deep learning …
Operator Feature Neural Network for Symbolic Regression
Symbolic regression is a task aimed at identifying patterns in data and representing them
through mathematical expressions, generally involving skeleton prediction and constant …
through mathematical expressions, generally involving skeleton prediction and constant …
Unveiling the relationship between Fabry-Perot laser structures and optical field distribution via symbolic regression
In recent years, machine learning (ML) techniques have been shown to be effective in
accelerating the development process of optoelectronic devices. However, as “black box” …
accelerating the development process of optoelectronic devices. However, as “black box” …
Integrating Atomistic Insights with Circuit Simulations via Transformer-Driven Symbolic Regression
This paper introduces a groundbreaking framework that establishes a cohesive link between
first principles-based simulations and circuit-level analyses using a machine learning-based …
first principles-based simulations and circuit-level analyses using a machine learning-based …