Learning from Imperfect Data: Robust Inference of Dynamic Systems Using Simulation-Based Generative Model

System inference for nonlinear dynamic models, represented by ordinary differential equations (ODEs), remains a significant challenge in many fields, particularly when the data are noisy, sparse, or partially observable. In this paper, we propose a Simulation-based Generative Model for Imperfect Data (SiGMoID), that enables precise and robust inference for dynamic systems. The proposed approach integrates two key methods: (1) HyperPINN, and (2) W-GAN. We demonstrate that SiGMoID quantifies data