Ever wondered what triggers sudden, dramatic shifts in complex systems? Our research takes a thrilling Machine Learning adventure to unravel the mysteries of “tipping points” - those critical junctures where small changes lead to big, often irreversible consequences. Read the paper: Nature Communications 15, 4117. Full blog post at Springer Nature Research Communities

Intro

Imagine a financial market teetering on the brink of a bubble or crises, an - apparently drowsy- disease outbreak escalating on a social network or a seemingly stable climate tipping into a cascade of extremes: scorching heat, vanishing water, and rising seas. These scenarios highlight the challenges of studying tipping points in complex systems. Traditional methods, like high-fidelity agent-based models (ABMs), offer valuable insights, but their computational demands can be a real roadblock.

This is where our research steps in. We propose a groundbreaking framework that harnesses the power of Machine Learning to analyze tipping points in large-scale ABMs. Here’s the magic: we extract low-dimensional “hidden patterns” from the data, allowing us to develop efficient models that capture the key dynamics near tipping points.

The Big Picture: Our approach offers several benefits

• Reduced Complexity: By focusing on the core dynamics, we significantly decrease computational costs compared to traditional methods.
• Unveiling Secrets: We identify the underlying mechanisms driving tipping points, providing valuable insights for prevention or mitigation.
• Quantifying the Unlikely: We calculate the probability of rare, catastrophic events triggered near tipping points.

Description

In the recent publication in Nature Communications (Fabiani et al. 2024), the Professor Kevrekidis group in collaboration with the Professor Siettos group, implement an efficient machine learning framework bridging numerical analysis, neural networks, Gaussian processes and the equation-free framework. Our work paves the way for estimating probabilities of rare events in complex systems, and detect tipping points/catastrophic shifts.

The developed code is available in a Gitlab repository.

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Read the paper: Nature Communications 15, 4117. Full blog post at Springer Nature Research Communities