2023/03/27 by A. C. Bekar, Bekar, A. C., E. Haghighat +3 · 1 citation
Engineering · Physics and Astronomy · #Electromagnetic Scattering and Analysis #Electromagnetic Simulation and Numerical Methods #FOS: Computer and information sciences #Machine Learning (cs.LG) #Numerical methods in engineering
paper · pdf · doi:10.48550/arxiv.2303.15631
openalex publication_date 2023/03/27 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28
This study proposes a novel framework for learning the underlying physics of phenomena with moving boundaries. The proposed approach combines Ensemble SINDy and Peridynamic Differential Operator (PDDO) and imposes an inductive bias assuming the moving boundary physics evolve in its own corotational coordinate system. The robustness of the approach is demonstrated by considering various levels of noise in the measured data using the 2D Fisher-Stefan model. The confidence intervals of recovered coefficients are listed, and the uncertainties of the moving boundary positions are depicted by obtaining the solutions with the recovered coefficients. Although the main focus of this study is the Fisher-Stefan model, the proposed approach is applicable to any type of moving boundary problem with a smooth moving boundary front without a mushy region. The code and data for this framework is available at: https://github.com/alicanbekar/MBPDDO-SINDy.