arXiv:2502.02205v4 Announce Type: replace Abstract: The application of deep learning for partial differential equation (PDE)-constrained control is gaining increasing attention. However, existing methods rarely consider safety requirements crucial in real-world applications. To address this limitation, we propose Safe Diffusion Models for PDE Control (SafeDiffCon), which introduce the uncertainty quantile as model uncertainty quantification to achieve optimal control under safety constraints through both post-training and inference phases. Firstly, our approach post-trains a pre-trained diffus
The increasing sophistication of deep learning and control theory is enabling new approaches to complex system management, particularly as AI safety becomes a critical concern in real-world applications.
This development addresses a key limitation in AI control by integrating safety and uncertainty quantification, paving the way for more reliable and deployable AI systems in critical infrastructure and industrial processes.
The ability to achieve optimal control under safety constraints using diffusion models introduces a new paradigm for designing AI-driven control systems, shifting from purely performance-driven to safety-aware methodologies.
- · Industrial automation
- · Robotics
- · AI safety researchers
- · Critical infrastructure operators
- · Developers of unsafe AI control systems
Enhances the trustworthiness and public acceptance of AI in high-stakes environments.
Accelerates the deployment of AI in sectors previously resistant due to safety concerns, such as energy grids or advanced manufacturing.
Could lead to the establishment of new regulatory frameworks and industry standards specifically for safety-critical AI control systems.
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Read at arXiv cs.LG