ERFit: Entropic Regression Fit Matlab Package, for Data-Driven System Identification of Underlying Dynamic Equations

2020/10/05 by Abd AlRahman R. AlMomani, Erik M. Bollt, AlMomani, Abd AlRahman +1 · 1 citation
Computer Science · Engineering · Physics and Astronomy · #Computation (stat.CO) #Computation and Language (cs.CL) #Control Systems and Identification #Dynamical Systems (math.DS) #Evolutionary Algorithms and Applications #FOS: Computer and information sciences #FOS: Mathematics #Gaussian Processes and Bayesian Inference #Machine Learning (stat.ML) #Model Reduction and Neural Networks #Neural Networks and Applications

paper · pdf · doi:10.48550/arxiv.2010.02411

openalex publication_date 2020/10/06 · openalex created_date 2022/07/25 · openalex updated_date 2026/07/28

Abstract

Data-driven sparse system identification becomes the general framework for a wide range of problems in science and engineering. It is a problem of growing importance in applied machine learning and artificial intelligence algorithms. In this work, we developed the Entropic Regression Software Package (ERFit), a MATLAB package for sparse system identification using the entropic regression method. The code requires minimal supervision, with a wide range of options that make it adapt easily to different problems in science and engineering. The ERFit is available at https://github.com/almomaa/ERFit-Package

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