🌀 Category

nlin.AO

228 papers

The intersection of nonlinear dynamics and artificial intelligence, often referred to as Nlin — Ao, explores how complex, unpredictable systems interact with modern machine learning models. This rapidly evolving field investigates whether neural networks can better predict chaotic phenomena, from weather patterns to financial markets, by learning the underlying mathematical rules that govern them. It bridges the gap between theoretical physics and computational data science, offering fresh perspectives on how we model uncertainty in a changing world.

At Gist.Science, we track every new preprint in this category as it appears on arXiv, ensuring you stay ahead of the curve without needing to decipher dense academic jargon. For each submission, our team generates both a detailed technical summary for experts and a clear, plain-language explanation for broader audiences. Below are the latest papers in Nlin — Ao, curated to help you understand the cutting edge of chaos and computation.

🌀 nonlinear sciences

Adaptive High-Level Tight Control of Prostate Cancer: A Path from From Terminal Disease to Chronic Condition

This paper proposes a Stackelberg game-theoretic framework utilizing Bayesian optimization to identify an adaptive high-level tight control (HLTC) chemotherapy strategy for metastatic prostate cancer, demonstrating that precise drug delivery based on closely spaced biomarker triggers can significantly prolong survival and potentially transform the disease from terminal to chronic.

Trung V. Phan, Shengkai Li, Luciana Sarabia, Caroline N. Cappetto, Benjamin Howe, Sarah R. Amend, Kenneth J. Pienta, Joe (…)2026-07-21
🌀 nonlinear sciences

Ant swarm functional control via stigmergic Reinforcement Learning agents

This paper proposes a novel framework using centralized-training, decentralized-execution Reinforcement Learning agents that interact with an ant swarm solely through a shared pheromone field to successfully shift the system's phase transition and induce ordered trail formation in regimes typically dominated by randomness.

Alessio Pitteri, Andrea Guizzo, Laura Ferrarotti, Bruno Lepri, Riccardo Gallotti2026-07-21
🌀 nonlinear sciences

Instability in Complex Oscillator Networks: Limitations and Potentials of Network Measures and Machine Learning

This study demonstrates that while both traditional network measures and machine learning models can accurately predict stability within specific oscillator network ensembles, their inability to generalize across different structural configurations reveals fundamental limitations in using these approaches to reliably identify the underlying structural causes of instability.

Christian Nauck, Michael Lindner, Nora Molkenthin, Jürgen Kurths, Eckehard Schöll, Jörg Raisch, Frank Hellmann2026-07-20
🌀 nonlinear sciences

Diffusion-induced instabilities promote cooperation in eco-evolutionary networks

This paper demonstrates that in eco-evolutionary public goods games on complex networks, the combination of asymmetric diffusion (where defectors move faster than cooperators) and heterogeneous connectivity induces symmetry-breaking transitions and bifurcations that foster the emergence of localized cooperative clusters, particularly among highly connected nodes.

Sourav Roy, Md Sayeed Anwar, Timoteo Carletti, Matjaz Perc, Dibakar Ghosh2026-07-20
🌀 nonlinear sciences

Redefining Fitness: Inference, Information and Phase Transitions in Evolutionary Dynamics

This paper resolves fundamental issues in evolutionary theory by redefining fitness as a Bayesian likelihood, demonstrating that natural selection acts to maximize the mutual information between population structure and environmental statistics, thereby establishing information maximization as the governing principle of evolution.

Luís MA Bettencourt, Brandon J Grandison, Jordan T Kemp2026-07-16✓ Author reviewed ⓘ
🌀 nonlinear sciences

Optimal control for phase locking of synchronized oscillator populations via dynamical reduction techniques

This paper presents a framework combining dynamical reduction techniques (Ott-Antonsen ansatz and phase-amplitude reduction) with optimal control theory to derive optimal periodic inputs that rapidly resynchronize the collective phase of coupled oscillator populations after a sudden phase shift, while simultaneously evaluating the impact on mutual synchrony.

Narumi Fujii, Hiroya Nakao2026-07-16