arXiv:2606.11629v1 Announce Type: cross Abstract: This manuscript proposes an integral formulation of the newly defined quadratic embedding method for identifying nonlinear systems (QENDy). In the original algorithm, trajectory data points along with their time derivatives are used. Methods for calculating time derivatives make the algorithm sensitive to noise. Our integral formulation does not use the time derivatives. This results in a more robust method to learn the dynamics.
The continuous drive for more robust and efficient machine learning models in scientific domains, especially concerning real-world noisy data, necessitates innovations like this integral formulation.
This development improves noise robustness in nonlinear system identification, crucial for AI applications in engineering and scientific discovery, where data quality often varies.
The identification of nonlinear systems becomes more reliable by bypassing the sensitive process of calculating time derivatives, leading to more accurate dynamic models from noisy trajectory data.
- · AI researchers
- · Robotics engineers
- · Control system designers
- · Industries relying on complex system modeling
- · Methods heavily reliant on noisy derivative calculations
- · Less robust system identification techniques
System identification for real-world applications becomes more feasible and accurate due to enhanced noise robustness.
Improved models could lead to more effective control systems and more precise predictive maintenance in industrial settings.
Accelerated deployment of AI in physically complex environments where sensors inherently produce noisy data.
This signal links to a primary source. Continuum Brief monitors and indexes it as part of the live intelligence stream — we do not republish source content.
Read at arXiv cs.LG