5 papers
Learning Interaction Kernels from Collective Steady States
Baoli Hao, Mauro Maggioni, Ming Zhong
We propose a learning procedure for system identification in interacting particle systems from single-snapshot observations of collective behaviors, unlike existing approaches that…
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…
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…
Interacting Particle Systems on Networks: joint inference of the network and the interaction kernel
Quanjun Lang, Xiong Wang, Fei Lu +1
Modeling multi-agent systems on networks is a fundamental challenge in a wide variety of disciplines. Given data consisting of multiple trajectories, we jointly infer the (weighted…
Nonlinear model reduction for slow-fast stochastic systems near unknown invariant manifolds
Felix X. -F. Ye, Sichen Yang, Mauro Maggioni
We introduce a nonlinear stochastic model reduction technique for high-dimensional stochastic dynamical systems that have a low-dimensional invariant effective manifold with slow d…