Applied Math · Mathematics & Statistics
Dynamical Systems
The newest papers in Dynamical Systems that ReadSearch has read, newest first. Each links to its own page, with its authors, date, where it was published and the original.
56 papers, from 8 September 2026 to 5 November 2026: 56 arXiv preprints.
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Newest papers, page 2
Generic finiteness of solutions to parametrized systems of generalized polynomial inequalities
We study parametrized systems of $\mathit{generalized}$ polynomial inequalities, $(c \circ x^B) \in C$, for a positive variable vector $x \in \mathbb{R}^n_>$, a $\mathit{real}$ exponent matrix $B…Adding Heavy-Ball Momentum to Distributed Constrained Optimization for Energy Resource Management Subject to Time-Delays and Quantization
This paper studies a hybrid discrete-time signed gradient-consensus method with heavy-ball momentum, paired communication delays, and fixed logarithmic quantization.SALTO: Solver-Anchored Lifted Transfer Operators for Nonlinear Event Inference
Learned dynamical models can accurately predict trajectories while producing unreliable sensitivities when differentiated or transposed, limiting their use in inverse problems and scientific…Contractor Filters
We introduce Contractor Filters (CFs), a set-membership architecture for nonlinear continuous-time state estimation with bounded uncertainties and sporadic outliers.Asymptotic Analysis of Discrete-Time Self-Attention Dynamics in Transformers
In machine learning, a self-attention dynamics describes the attention mechanism of transformers, and is obtained treating the layer index of a transformer as time index.Understanding Latent-Dimension Scaling in Dynamical-System Learning through Spectral Reliability
In deep learning, approximation theory motivates increasing representation size.Bilevel optimization for data-driven learning of Koopman embeddings using kernel-based autoencoders
Koopman operator theory provides a linear framework for analyzing nonlinear dynamical systems and has become a major tool for data-driven modeling.Koopman Observers for Diffusion Acceleration: Correcting Feature Forecasts with Shallow Measurements
Feature caching accelerates diffusion sampling by replacing expensive network evaluations with predictions from previously computed activations.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.A KKL Observer Perspective on Reservoir Computing
Reservoir computing (RC) is a machine learning technique for data-driven modeling of dynamics for forecasting and control, primarily studied in computer science and physics literature with promising…Efficiently and Reliably Measuring Information Processing Capacity in Dynamical Systems via Kernels
Information Processing Capacity (IPC) is a powerful system-agnostic metric for quantifying a dynamical system's computational capability and is widely used in quantum reservoir computing.Finite-Data Safety Informativity Under Dynamic Asymmetric Actuation
When the system model is not fully known, measurement error and limited excitation can leave several models consistent with the same finite data.Wasserstein Gradient Flows and Forward-Only Diffusion Are Not Enough for Multimodal Sampling
There has been a proliferation of sampling algorithms based on Wasserstein gradient flows (WGF) and forward-only diffusion processes (FODP), often accompanied by theoretical guarantees of…Learning Closure of Dynamical Systems with Kernel Ridge Regression
We develop a closure modeling framework for identifying missing components of dynamical systems using Kernel Ridge Regression (KRR).Nonlocal Hamiltonian Dynamics on Sparse Lévy Graphs: Spectral Analysis and Multimodal Sampling
We develop a sparse graph method for transporting probability mass toward multimodal target distributions through damped nonlocal Hamiltonian dynamics.Dynamics to decision: A mathematical theory of Lyapunov spectra and decision boundaries in deep classifiers
A deep classifier is defined not only by the decision it produces, but also by the sequence of transformations through which that decision is formed.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.Cluster Attention Neural Operators for Solving Parametric Partial Differential Equations
Traditional simulations of parametric partial differential equations (PDEs) rely on repetitive computations for each parameter, which makes high-fidelity design impractical.Admissibility-Preserving Control for Multi-Input Systems with Joint Capacity Constraints
This paper addresses the control of multi-input strict-feedback nonlinear systems subject to a joint capacity constraint, in which the admissible input set is a coupled subset of the individual…Identifiability Guarantees for Drivers and Dynamics of Delayed Physical Systems
A wide range of methods have been proposed, including physics-informed neural networks, which are powerful but do not guarantee identifiability of the dynamics, symbolic regression, which requires a…