General & Applied Physics · Physics & Astronomy
Chaotic Dynamics
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41 papers, from 4 September 2026 to 31 October 2026: 41 arXiv preprints.
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A Response Theory Probe for Learned Stochastic AI Simulators, Tested on Lorenz-63
Machine-learning emulators of chaotic and stochastic systems are usually validated on forecast skill and long-run statistics.Kardar-Parisi-Zhang superdiffusion in chaotic quantum circuits
We construct a family of chaotic brickwork quantum circuits exhibiting superdiffusive transport with dynamical exponent $z=3/2$ in the full Kardar--Parisi--Zhang (KPZ) universality class.Classical chaotic signatures in Krylov space
In recent years, Krylov space approaches have emerged as a useful framework for characterizing dynamical chaos in a wide variety of physical systems.The interface of data assimilation and machine learning
Data assimilation (DA) is the process of combining forecasts from a model with observations in order to optimally estimate the state of a system.Do Neural PDE Solvers Learn the Right Dynamics?
Neural PDE solvers can achieve low prediction errors, but do they reproduce the dynamics of the systems they model?Physics is the Best Teacher: Consistency Learning for Time-Invariant Operators of Chaotic Dynamics
Accelerating the prediction of long-term behavior in chaotic systems is crucial in scientific computing.The Effect of Gait Stability Based on Two Types of Impact Strategies for Two-Link Walking and Brachiating Robots
In this paper, we explore the impulsive dynamics common to single-joint, two-link models of walking and brachiating gaits with respect to slope and switching time.From Redundancy to Minimality: Fixed-Point-Guided Hierarchical Reduction of Learned Piecewise-Linear Dynamics
Understanding a nonlinear dynamical system from time series requires not only reproducing its trajectories, but also identifying a simple representation that preserves its essential dynamical…Learning Chaos Without Seeing Chaos: Extrapolation of Global Dynamics in Autoregressive Transformers
Autoregressive models are trained to predict a system's behavior one step at a time, and recursive generation allows the learned dynamics to unfold over long horizons.TopTimeNet: Topologically-assisted time-series classification model
Distinguishing periodic from chaotic dynamics in a time series is a fundamental challenge in both physics and engineering.Trotterisation Thresholds and Dissipative Quantum Chaos in Open-System Digital Quantum Simulation
Digital quantum simulation (DQS) is one of the most promising applications of quantum computing.Context-dependent time-series prediction via HyperReservoirs
Time series prediction is a common application of reservoir computing.Speed-Fisher Information: Chaos and Irreversibility in Classical and Quantum Dynamics
We propose a unified perspective on classical and quantum chaos based on the sensitivity of a system's stationary states to slow driving.Massively Parallel Reinforcement Learning with a Chaotic Reconfigurable Clockless Chip
Hardware accelerators based on physical dynamical systems offer an attractive route toward energy-efficient reinforcement learning applications.Orbital Error Dynamics: Self-Organized Criticality, Ephemeral Parameter Resonance, and Non-Linear Biological Ontologies in Zero-Storage Neural Synthesis
Modern deep neural networks treat parameters as static floating-point matrices stored in physical memory, incurring Von Neumann memory bottlenecks and representation collapse.Onset of random-matrix statistics in the Yukawa-Sachdev-Ye-Kitaev model: Spectral correlations and Krylov complexity
The Yukawa-Sachdev-Ye-Kitaev (YSYK) model, a solvable model of non-Fermi liquids with a proposed cavity-QED realization, couples $N$ complex fermions to $M$ bosons of frequency $ω_0$ through random…PR-Smoother: Simulator-Preserving Non-Gaussian Smoothing for Data Assimilation
Many physical data assimilation (DA) workflows require smoothing methods that represent non-Gaussian posteriors over physical state variables, scale to high-dimensional simulators, train from…Inference of Unknown Dynamical Components Using Next Generation Reservoir Computing: From Chaotic Systems to Climate Data
We investigate next generation reservoir computing (NGRC) as a data-driven approach for inferring unseen components of dynamical systems.Learning-Induced Dynamical Transition in Recurrent Neural Networks
Learning in recurrent neural networks can fundamentally reshape their underlying dynamics, transforming initially chaotic activity into stable task-dependent behavior.Optimizing Geoengineering Interventions Using Differentiable Climate Models
The deployment of a geoengineering program to cool Earth's climate may be imminent.
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