mlsanityData to 14 Oct 2026

Tasks Makyth Models: Machine Learning Assisted Surrogates for Tipping Points

arXiv 2309.14334 ↗First posted 25 Sep 2023cs.LG cs.NA math.DScited by 3

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Abstract (arXiv)

We present a machine learning (ML)-assisted framework bridging manifold learning, neural networks, Gaussian processes, and Equation-Free multiscale modeling, for (a) detecting tipping points in the emergent behavior of complex systems, and (b) characterizing probabilities of rare events (here, catastrophic shifts) near them. Our illustrative example is an event-driven, stochastic agent-based model (ABM) describing the mimetic behavior of traders in a simple financial market. Given high-dimensional spatiotemporal data -- generated by the stochastic ABM -- we construct reduced-order models for the emergent dynamics at different scales: (a) mesoscopic Integro-Partial Differential Equations (IPDEs); and (b) mean-field-type Stochastic Differential Equations (SDEs) embedded in a low-dimensional latent space, targeted to the neighborhood of the tipping point. We contrast the uses of the different models and the effort involved in learning them.

Key facts. Cited by 3 papers in the map (data through 14 Oct 2026). Builds on 23 of its 89 references that are in the map. First posted on arXiv 25 Sep 2023.

Builds on

23 of 89 references.

References · 23

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11 more
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Cited by year

Citing papers per year: 2023: 2; 2024: 1.

3 papers.

  1. 2024Random Projection Neural Networks of Best Approximation: Convergence theory and practical applicationscited by 11
  2. 2023Tipping Points of Evolving Epidemiological Networks: Machine Learning-Assisted, Data-Driven Effective Modelingcited by 1
  3. 2023Machine Learning for the identification of phase-transitions in interacting agent-based systems: a Desai-Zwanzig examplecited by 0