2 citations · 2 across the 4 of their papers we have counts for
5 papers
Data-driven Learning of Interaction Laws in Multispecies Particle Systems with Gaussian Processes: Convergence Theory and Applications
Jinchao Feng, Charles Kulick, Sui Tang
We develop a Gaussian process framework for learning interaction kernels in multi-species interacting particle systems from trajectory data. Such systems provide a canonical settin…
On the Convergence and Size Transferability of Continuous-depth Graph Neural Networks
Mingsong Yan, Charles Kulick, Sui Tang
Continuous-depth graph neural networks, also known as Graph Neural Differential Equations (GNDEs), combine the structural inductive bias of Graph Neural Networks (GNNs) with the co…
A Sparse Bayesian Learning Algorithm for Estimation of Interaction Kernels in Motsch-Tadmor Model
Jinchao Feng, Sui Tang
In this paper, we investigate the data-driven identification of asymmetric interaction kernels in the Motsch-Tadmor model based on observed trajectory data. The model under conside…
Sparse identification of nonlocal interaction kernels in nonlinear gradient flow equations via partial inversion
Jose A. Carrillo, Gissell Estrada-Rodriguez, Laszlo Mikolas +1
We address the inverse problem of identifying nonlocal interaction potentials in nonlinear aggregation-diffusion equations from noisy discrete trajectory data. Our approach involve…
Data-Driven Model Selections of Second-Order Particle Dynamics via Integrating Gaussian Processes with Low-Dimensional Interacting Structures
Jinchao Feng, Charles Kulick, Sui Tang
In this paper, we focus on the data-driven discovery of a general second-order particle-based model that contains many state-of-the-art models for modeling the aggregation and coll…