Certifiably robust neural ODE with learning-based barrier function

Neural Ordinary Differential Equations (ODEs) have gained traction across applications, yet certified robustness remains limited. This letter proposes training a neural ODE using barrier functions, demonstrating improved robustness on classification tasks. We also provide a first generalization guarantee of robustness against adversarial attacks via a wait-and-judge scenario approach.