Abstract
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 evolves 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 an intermediate zone of two states. The code and data for this framework is available at: https://github.com/alicanbekar/MB_PDDO-SINDy.
Original language | English (US) |
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Journal | Engineering with Computers |
DOIs | |
State | Accepted/In press - 2024 |
Keywords
- Ensemble learning
- Model discovery
- Moving boundary models
- Peridynamic differential operator
- SINDy
ASJC Scopus subject areas
- Software
- Modeling and Simulation
- General Engineering
- Computer Science Applications