LeARN: Learnable and Adaptive Representations for Nonlinear Dynamics in System Identification
arXiv:2412.12036v2 Announce Type: replace Abstract: System identification, the process of deriving mathematical models of dynamical systems from observed input-output data, has undergone a paradigm shift with the advent of learning-based methods. Addressing the intricate challenges of data-driven discovery in nonlinear dynamical systems, these methods have garnered significant attention. Among them, Sparse Identification of Nonlinear Dynamics (SINDy) has emerged as a transformative approach, distilling complex dynamical behaviors into interpretable linear combinations of basis functions. Howev
The continuous evolution of AI and machine learning techniques, particularly in understanding complex systems, is driving new advancements in system identification.
Improved system identification for nonlinear dynamics will accelerate the development and control of complex AI systems, robotics, and scientific modeling, reducing the need for extensive manual tuning.
The ability to automatically learn and adapt representations for nonlinear dynamics makes the development of autonomous systems more robust and efficient.
- · AI/ML researchers
- · Robotics industry
- · Control systems engineers
- · Scientific research institutions
- · Traditional model-based control methods
- · Trial-and-error development processes
More accurate and adaptive models for complex real-world systems, from manufacturing to biological processes.
Faster deployment and higher performance of AI agents and autonomous robots in dynamic environments.
Potential for breakthroughs in areas like personalized medicine or climate modeling due to enhanced system understanding.
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Read at arXiv cs.LG