arXiv:2505.17868v2 Announce Type: replace Abstract: We present the first provable method for identifying symmetric linear dynamical systems (LDS) with accuracy guarantees that are independent of the systems' state dimension or effective memory. Our approach builds upon recent work that represents symmetric LDSs as convolutions learnable via fixed spectral transformations. We show how to invert this representation, thereby recovering an LDS model from its spectral transform and yielding an end-to-end convex optimization procedure. This distillation preserves predictive accuracy while enabling c
This research provides a more efficient and provable method for identifying linear dynamical systems, an underlying component in many AI and control applications, indicating a current focus on foundational improvements in AI model reliability and efficiency.
A provably accurate and computationally efficient method for system identification can accelerate AI development, especially in areas requiring robust control and understanding of complex, temporal data, reducing the computational burden for training and deployment.
The ability to distill complex dynamical systems into simpler, provable models with guaranteed accuracy changes how researchers and developers can approach system learning, potentially making AI systems more reliable and less resource-intensive.
- · AI researchers
- · Robotics companies
- · Control systems engineers
- · Computational infrastructure providers
- · Inefficient black-box modeling techniques
- · Compute-intensive deep learning approaches for control
More efficient and reliable AI models for dynamic environments become feasible.
Reduced computational requirements for identifying and deploying complex control systems lead to faster innovation cycles.
Enhanced AI fidelity across various applications (e.g., autonomous systems, scientific discovery) without proportional increases in computational cost, potentially impacting compute supply chain dynamics.
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Read at arXiv cs.LG