Cover Image

Machine learning approach to detect dynamical states from recurrence measures

Fuente: arXiv

Saved in:

Bibliographic Details
Main Authors:	Thakur, Dheeraja, Mohan, Athul, Ambika, G., Meena, Chandrakala
Format:	Preprint
Published:	2024
Subjects:	Data Analysis, Statistics and Probability Machine Learning
Online Access:	Acceder al recurso
Tags:	Add Tag No Tags, Be the first to tag this record!

Similar Items

An information field theory approach to Bayesian state and parameter estimation in dynamical systems
by: Hao, Kairui, et al.
Published: (2023)

Deep learning for classifying dynamical states from time series via recurrence plots
by: Mohan, Athul, et al.
Published: (2025)

Statistical limits of correlation detection in trees
by: Ganassali, Luca, et al.
Published: (2022)

Multifractal and recurrence measures from meteorological data of climate zones in India
by: Bejoy, Joshin John, et al.
Published: (2023)

Learning thermodynamically constrained equations of state with uncertainty
by: Sharma, Himanshu, et al.
Published: (2023)

Learning from the past: predicting critical transitions with machine learning trained on surrogates of historical data
by: Ma, Zhiqin, et al.
Published: (2024)

Physics-constrained polynomial chaos expansion for scientific machine learning and uncertainty quantification
by: Sharma, Himanshu, et al.
Published: (2024)

Exploring how deep learning decodes anomalous diffusion via Grad-CAM
by: Bae, Jaeyong, et al.
Published: (2024)

Towards certification: A complete statistical validation pipeline for supervised learning in industry
by: Lacasa, Lucas, et al.
Published: (2024)

Uncertainty estimation via ensembles of deep learning models and dropout layers for seismic traces
by: Messuti, Giovanni, et al.
Published: (2024)

Comparative study of machine learning and statistical methods for automatic identification and quantification in γ-ray spectrometry
by: Phan, Dinh Triem, et al.
Published: (2025)

Learning to solve Bayesian inverse problems: An amortized variational inference approach using Gaussian and Flow guides
by: Karumuri, Sharmila, et al.
Published: (2023)

Hierarchical geometric deep learning enables scalable analysis of molecular dynamics
by: Pengmei, Zihan, et al.
Published: (2025)

Reliable data clustering with Bayesian community detection
by: Neuman, Magnus, et al.
Published: (2025)

Comparative Analysis of Machine Learning-Based Imputation Techniques for Air Quality Datasets with High Missing Data Rates
by: Yan, Sen, et al.
Published: (2024)

Short-Term Forecasting of Energy Production and Consumption Using Extreme Learning Machine: A Comprehensive MIMO based ELM Approach
by: Voyant, Cyril, et al.
Published: (2025)

Exponentially improved efficient machine learning for quantum many-body states with provable guarantees
by: Che, Yanming, et al.
Published: (2023)

Out-of-Sample Hydrocarbon Production Forecasting: Time Series Machine Learning using Productivity Index-Driven Features and Inductive Conformal Prediction
by: Idris, Mohamed Hassan Abdalla, et al.
Published: (2025)

Development of a high-resolution indoor radon map using a new machine learning-based probabilistic model and German radon survey data
by: Petermann, Eric, et al.
Published: (2023)

Identifying topology of leaky photonic lattices with machine learning
by: Smolina, Ekaterina O., et al.
Published: (2023)

Tracking Dynamical Transitions using Link Density of Recurrence Networks
by: Jacob, Rinku, et al.
Published: (2024)

Tunable correlation retention: A statistical method for generating synthetic data
by: Jävergård, Nicklas, et al.
Published: (2024)

Self-learning signal classifier for decameter coherent scatter radars
by: Berngardt, Oleg, et al.
Published: (2025)

Learning dynamical systems with hit-and-run random feature maps
by: Mandal, Pinak, et al.
Published: (2025)

Fast reconstruction-based ROI triggering via anomaly detection in the CYGNO optical TPC
by: Amaro, F. D., et al.
Published: (2025)

Potential of Domain Adaptation in Machine Learning in Ecology and Hydrology to Improve Model Extrapolability
by: Shi, Haiyang
Published: (2024)

Learning effective good variables from physical data
by: Barletta, Giulio, et al.
Published: (2024)

SpecTUS: Spectral Translator for Unknown Structures annotation from EI-MS spectra
by: Hájek, Adam, et al.
Published: (2025)

Probabilistic Emissivity Retrieval from Hyperspectral Data via Physics-Guided Variational Inference
by: Tempelman, Joshua R., et al.
Published: (2025)

Stochastic Inference of Plate Bending from Heterogeneous Data: Physics-informed Gaussian Processes via Kirchhoff-Love Theory
by: Kavrakov, Igor, et al.
Published: (2024)

Trigger Optimization and Event Classification for Dark Matter Searches in the CYGNO Experiment Using Machine Learning
by: Amaro, F. D., et al.
Published: (2026)

Reconstructing dynamics from sparse observations with no training on target system
by: Zhai, Zheng-Meng, et al.
Published: (2024)

Bayesian symbolic regression: Automated equation discovery from a physicists' perspective
by: Guimera, Roger, et al.
Published: (2025)

Unsupervised learning of anomalous diffusion data
by: Muñoz-Gil, Gorka, et al.
Published: (2021)

Anticipating tipping in spatiotemporal systems with machine learning
by: Deb, Smita, et al.
Published: (2026)

Scalable Sparse Regression for Model Discovery: The Fast Lane to Insight
by: Golden, Matthew
Published: (2024)

Active Learning with Fully Bayesian Neural Networks for Discontinuous and Nonstationary Data
by: Ziatdinov, Maxim
Published: (2024)

DPGIIL: Dirichlet Process-Deep Generative Model-Integrated Incremental Learning for Clustering in Transmissibility-based Online Structural Anomaly Detection
by: Mei, Lin-Feng, et al.
Published: (2024)

Dreaming Learning
by: Londei, Alessandro, et al.
Published: (2024)

AI Meets Antimatter: Unveiling Antihydrogen Annihilations
by: Ferreira, Ashley, et al.
Published: (2024)