Beyond Prediction: Interval Neural Networks for Uncertainty-Aware System Identification

ArXi:2605.11460v1 Announce Type: new System identification (SysID) is critical for modeling dynamical systems from experimental data, yet traditional approaches often fail to capture nonlinear behaviors. While deep learning offers powerful tools for modeling such dynamics, incorporating uncertainty quantification is essential to ensure reliable predictions. This paper presents a systematic framework for constructing and