Automated Synthesis of Verified Neural Network Controllers from Linear Temporal Logic Specifications

Deep neural networks have demonstrated remarkable capabilities across diverse domains, yet their unpredictable behavior and potential for catastrophic failures severely limit their deployment in safety-critical applications such as critical infrastructure control. This limitation is particularly frustrating as neural networks often outperform traditional control systems in handling complex, nonlinear dynamics and adapting to varying conditions. We present a pipeline that transforms Linear Temporal Logic (LTL) specifications into verified deep neural networks, guaranteeing that the synthesized controllers satisfy all safety and liveness properties by construction. By bridging formal methods with modern machine learning, our approach enables the use of neural networks in safety-critical systems with mathematical guarantees of correctness, opening new possibilities for intelligent control in domains where failure is not an option.