3 papers
stat.ML2026
Learning interacting particle systems from unlabeled data
Viska Wei, Fei Lu
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…
stat.ML2026
Learning Multi-type heterogeneous interacting particle systems
Quanjun Lang, Xiong Wang, Fei Lu +1
We propose a framework for the joint inference of network topology, multi-type interaction kernels, and latent type assignments in heterogeneous interacting particle systems from m…
math.PR2025
Probabilistic cellular automata with local transition matrices: synchronization, ergodicity, and inference
Erhan Bayraktar, Fei Lu, Mauro Maggioni +2
We introduce a new class of probabilistic cellular automata that are capable of exhibiting rich dynamics such as synchronization and ergodicity and can be easily inferred from data…