Discover governing differential equations from evolving systems
This work addresses the challenge of real-time equation discovery for evolving systems, which is incremental as it adapts existing methods to handle streaming data.
The paper tackles the problem of discovering governing differential equations from streaming data, where current methods fail by processing entire datasets. The proposed online method successfully discovers ODEs and PDEs from streaming data and identifies change points in evolving systems, demonstrating competitiveness in three hybrid and two switching linear systems.
Discovering the governing equations of evolving systems from available observations is essential and challenging. In this paper, we consider a new scenario: discovering governing equations from streaming data. Current methods struggle to discover governing differential equations with considering measurements as a whole, leading to failure to handle this task. We propose an online modeling method capable of handling samples one by one sequentially by modeling streaming data instead of processing the entire dataset. The proposed method performs well in discovering ordinary differential equations (ODEs) and partial differential equations (PDEs) from streaming data. Evolving systems are changing over time, which invariably changes with system status. Thus, finding the exact change points is critical. The measurement generated from a changed system is distributed dissimilarly to before; hence, the difference can be identified by the proposed method. Our proposal is competitive in identifying the change points and discovering governing differential equations in three hybrid systems and two switching linear systems.