mlsanityData to 14 Oct 2026

Multi-Scale Simulation of Complex Systems: A Perspective of Integrating Knowledge and Data

arXiv 2306.10275 ↗First posted 17 Jun 2023eess.SY cs.AI cs.LGcited by 5

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Abstract (arXiv)

Complex system simulation has been playing an irreplaceable role in understanding, predicting, and controlling diverse complex systems. In the past few decades, the multi-scale simulation technique has drawn increasing attention for its remarkable ability to overcome the challenges of complex system simulation with unknown mechanisms and expensive computational costs. In this survey, we will systematically review the literature on multi-scale simulation of complex systems from the perspective of knowledge and data. Firstly, we will present background knowledge about simulating complex system simulation and the scales in complex systems. Then, we divide the main objectives of multi-scale modeling and simulation into five categories by considering scenarios with clear scale and scenarios with unclear scale, respectively. After summarizing the general methods for multi-scale simulation based on the clues of knowledge and data, we introduce the adopted methods to achieve different objectives. Finally, we introduce the applications of multi-scale simulation in typical matter systems and social systems.

Key facts. Cited by 5 papers in the map (data through 14 Oct 2026). Builds on 40 of its 216 references that are in the map. First posted on arXiv 17 Jun 2023.

Builds on

40 of 216 references.

References · 40

  1. 2015Deep Residual Learning for Image Recognitioncited by 52,096
  2. 2022Generative Adversarial Networkscited by 5,627
  3. 2018Neural Ordinary Differential Equationscited by 3,779
  4. 2013Playing Atari with Deep Reinforcement Learningcited by 3,274
  5. 2020Learning to Simulate Complex Physics with Graph Networkscited by 880
  6. 2018Hierarchical Graph Representation Learning with Differentiable Poolingcited by 787
  7. 2021Machine learning accelerated computational fluid dynamicscited by 471
  8. 2021Modern Koopman Theory for Dynamical Systemscited by 276
  9. 2014Dynamic mode decomposition with controlcited by 129
  10. 2020Multiscale Simulations of Complex Systems by Learning their Effective Dynamicscited by 86
  11. 2017Time-lagged autoencoders: Deep learning of slow collective variables for molecular kineticscited by 65
  12. 2021The physics of higher-order interactions in complex systemscited by 63
28 more
  1. 2014An exact mapping between the Variational Renormalization Group and Deep Learningcited by 56
  2. 2018Neural Network Renormalization Groupcited by 48
  3. 2021Simulating Continuum Mechanics with Multi-Scale Graph Neural Networkscited by 40
  4. 2017Reconstruction of three-dimensional porous media using generative adversarial neural networkscited by 36
  5. 2019Coarse-scale PDEs from fine-scale observations via machine learningcited by 35
  6. 2017Mutual Information, Neural Networks and the Renormalization Groupcited by 33
  7. 2018A deep material network for multiscale topology learning and accelerated nonlinear modeling of heterogeneous materialscited by 31
  8. 2018Coarse-Graining Auto-Encoders for Molecular Dynamicscited by 30
  9. 2017Variational Encoding of Complex Dynamicscited by 21
  10. 2018Meta-modeling game for deriving theoretical-consistent, micro-structural-based traction-separation laws via deep reinforcement learningcited by 19
  11. 2020Revealing hidden dynamics from time-series data by ODENetcited by 17
  12. 2020Deep learning of contagion dynamics on complex networkscited by 16
  13. 2019Exploring the 3D architectures of deep material network in data-driven multiscale mechanicscited by 14
  14. 2020On-the-fly construction of surrogate constitutive models for concurrent multiscale mechanical analysis through probabilistic machine learningcited by 14
  15. 2020GINNs: Graph-Informed Neural Networks for Multiscale Physicscited by 12
  16. 2021Noise-injected analog Ising machines enable ultrafast statistical sampling and machine learningcited by 11
  17. 2022Towards Fast Simulation of Environmental Fluid Mechanics with Multi-Scale Graph Neural Networkscited by 11
  18. 2020Sparse Identification of Slow Timescale Dynamicscited by 10
  19. 2021MixSeq: Connecting Macroscopic Time Series Forecasting with Microscopic Time Series Datacited by 9
  20. 2018Deep Multiscale Model Learningcited by 8
  21. 2019High Performance Monte Carlo Simulation of Ising Model on TPU Clusterscited by 7
  22. 2016Multi-scale Perimeter Control Approach in a Connected-Vehicle Environmentcited by 5
  23. 2019Finding the right scale of a network: Efficient identification of causal emergence through spectral clusteringcited by 4
  24. 2021Strategic COVID-19 vaccine distribution can simultaneously elevate social utility and equitycited by 3
  25. 2022REMuS-GNN: A Rotation-Equivariant Model for Simulating Continuum Dynamicscited by 3
  26. 2020Scaling of contact networks for epidemic spreading in urban transit systemscited by 2
  27. 2020Measuring the effect of Non-Pharmaceutical Interventions (NPIs) on mobility during the COVID-19 pandemic using global mobility datacited by 2
  28. 2020When Machine Learning Meets Multiscale Modeling in Chemical Reactionscited by 1

Built on by

Cited by year (2026 to 14 Oct)

Citing papers per year: 2024: 1; 2025: 3; 2026: 1.

5 papers.

  1. 2026Artificial Intelligence in Materials Science and Engineering: Current Landscape, Key Challenges, and Future Trajectoriecited by 5
  2. 2024TDNetGen: Empowering Complex Network Resilience Prediction with Generative Augmentation of Topology and Dynamicscited by 3
  3. 2025Application of Reduced-Order Models for Temporal Multiscale Representations in the Prediction of Dynamical Systemscited by 1
  4. 2025Predicting the Energy Landscape of Stochastic Dynamical System via Physics-informed Self-supervised Learningcited by 0
  5. 2025HPC-AI Coupling Methodology for Scientific Applicationscited by 0