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Found 5 papers, 0 papers with code
Data-Driven Model Selections of Second-Order Particle Dynamics via Integrating Gaussian Processes with Low-Dimensional Interacting Structures
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 collective behavior of interacting agents of similar size and body type.
Learning Collective Behaviors from Observation
We present a comprehensive examination of learning methodologies employed for the structural identification of dynamical systems.
Learning Interaction Variables and Kernels from Observations of Agent-Based Systems
Dynamical systems across many disciplines are modeled as interacting particles or agents, with interaction rules that depend on a very small number of variables (e. g. pairwise distances, pairwise differences of phases, etc...), functions of the state of pairs of agents.
Model Uncertainty and Correctability for Directed Graphical Models
Probabilistic graphical models are a fundamental tool in probabilistic modeling, machine learning and artificial intelligence.
Learning particle swarming models from data with Gaussian processes
As a byproduct, we show we can obtain a parametric learning rate in $M$ for the posterior marginal variance using $L^{\infty}$ norm, and the rate could also involve $N$ and $L$ (the number of observation time instances for each trajectory), depending on the condition number of the inverse problem.
