Neural Abstraction

Neural abstraction aims to create simplified, yet formally guaranteed, representations of complex nonlinear dynamical systems, often using neural networks. Current research focuses on developing efficient algorithms to synthesize these abstractions, exploring different neural network architectures (e.g., those using ReLU or sigmoidal activations) to balance precision and computational cost, and improving the tightness of the resulting approximations. This work is significant because it enables the verification of safety properties in systems previously intractable due to their complexity, with applications in areas requiring rigorous guarantees of system behavior.