Multiphysics discovery with moving boundaries using Ensemble SINDy and Peridynamic Differential Operator
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Abstract (arXiv)
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/MB_PDDO-SINDy.
Key facts. Cited by 3 papers in the map (data through 7 Oct 2026). Builds on 8 of its 47 references that are in the map. First posted on arXiv 27 Mar 2023.
Builds on
8 of 47 references.
References · 8
- 2020Weak SINDy For Partial Differential Equationscited by 154
- 2021Ensemble-SINDy: Robust sparse model discovery in the low-data, high-noise limit, with active learning and controlcited by 134
- 2020Physics-Guided Machine Learning for Scientific Discovery: An Application in Simulating Lake Temperature Profilescited by 59
- 2018On the Convergence of the SINDy Algorithmcited by 49
- 2020Automatic Differentiation to Simultaneously Identify Nonlinear Dynamics and Extract Noise Probability Distributions from Datacited by 36
- 2020Deep learning of free boundary and Stefan problemscited by 33
- 2020SINDy-BVP: Sparse Identification of Nonlinear Dynamics for Boundary Value Problemscited by 22
- 2021Discrete Frechet distance for closed curvescited by 2
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Citing papers per year: 2025: 1; 2026: 2.
3 papers.