Learning interacting particle systems from unlabeled data

ArXi:2604.02581v1 Announce Type: cross Learning the potentials of interacting particle systems is a fundamental task across various scientific disciplines. A major challenge is that unlabeled data collected at discrete time points lack trajectory information due to limitations in data collection methods or privacy constraints. We address this challenge by