arXiv:2606.19368v1 Announce Type: cross Abstract: In this work we investigate the role of neural architectures as implicit functional priors in control problems governed by ordinary differential equations. Rather than focusing on highly complex problems, our objective is to investigate architecture-dependent effects in controlled dynamical systems within the simplest physically interpretable settings possible. In particular, we study a controlled linear RLC electrical circuit and a nonlinear Duffing-type dynamical system. Both systems are analyzed first through classical optimal-control formul
The proliferation of AI in scientific and engineering domains is driving research into how neural networks can be more deeply integrated and understood within established control theory paradigms.
This research explores fundamental aspects of using AI as intrinsic components in physical control systems, which is critical for developing more robust and autonomous AI-driven engineering applications.
The understanding of neural network architectures not just as black boxes, but as explicit functional priors in control problems, potentially leading to more principled design and deployment of AI in controlled environments.
- · AI researchers
- · Control systems engineers
- · Robotics
- · Aerospace
- · Traditional optimal control methods (if not integrated with AI advancements)
Improved performance and adaptability of AI-driven control systems in dynamic physical environments.
Reduced need for extensive manual tuning and expert domain knowledge in complex system design.
Acceleration of autonomous system development across critical infrastructure and advanced manufacturing.
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Read at arXiv cs.LG