mlsanityData to 7 Oct 2026

Learning Individual Interactions from Population Dynamics with Discrete-Event Simulation Model

arXiv 2205.02332 ↗First posted 4 May 2022cs.LGcited by 0

Not on the map. Why?

Abstract (arXiv)

The abundance of data affords researchers to pursue more powerful computational tools to learn the dynamics of complex system, such as neural networks, engineered systems and social networks. Traditional machine learning approaches capture complex system dynamics either with dynamic Bayesian networks and state space models, which is hard to scale because it is non-trivial to prescribe the dynamics with a sparse graph or a system of differential equations; or a deep neural networks, where the distributed representation of the learned dynamics is hard to interpret. In this paper, we will explore the possibility of learning a discrete-event simulation representation of complex system dynamics assuming multivariate normal distribution of the state variables, based on the observation that many complex system dynamics can be decomposed into a sequence of local interactions, which individually change the system state only minimally but in sequence generate complex and diverse dynamics. Our results show that the algorithm can data-efficiently capture complex network dynamics in several fields with meaningful events.

Key facts. No citing papers in the map yet (data through 7 Oct 2026). Builds on 9 of its 32 references that are in the map. First posted on arXiv 4 May 2022.

Builds on

9 of 32 references.

References · 9

  1. 2018Relational inductive biases, deep learning, and graph networkscited by 1,341
  2. 2016Interaction Networks for Learning about Objects, Relations and Physicscited by 503
  3. 2018Deep Hidden Physics Models: Deep Learning of Nonlinear Partial Differential Equationscited by 233
  4. 2019Towards Physics-informed Deep Learning for Turbulent Flow Predictioncited by 203
  5. 2016A Compositional Object-Based Approach to Learning Physical Dynamicscited by 119
  6. 2013How to Construct Deep Recurrent Neural Networkscited by 64
  7. 2017Hierarchical Implicit Models and Likelihood-Free Variational Inferencecited by 13
  8. 2015Recurrent Gaussian Processescited by 9
  9. 2016Using Social Dynamics to Make Individual Predictions: Variational Inference with a Stochastic Kinetic Modelcited by 1

Built on by

Not cited yet.

Similar, not cited

Close in wording, no citation either way

  1. 2023Cheap and Deterministic Inference for Deep State-Space Models of Interacting Dynamical Systems
  2. 2025Learning Individual Behavior in Agent-Based Models with Graph Diffusion Networks
  3. 2024Generative Learning for Forecasting the Dynamics of Complex Systems
  4. 2023Neural Continuous-Discrete State Space Models for Irregularly-Sampled Time Series
  5. 2022Neural Lagrangian Schr"odinger Bridge: Diffusion Modeling for Population Dynamics
  6. 2026Graph Mamba Operator: A Latent Simulator for Interacting Particle Systems
  7. 2026Network World Models as Environments for Algorithm Design on Complex Systems
  8. 2020Nested Mixture of Experts: Cooperative and Competitive Learning of Hybrid Dynamical System