Imagen de Portada

Dynamical Mode Recognition of Turbulent Flames in a Swirl-stabilized Annular Combustor by a Time-series Learning Approach

Fuente: arXiv

Guardado en:

Detalles Bibliográficos
Autores principales:	Yang, Tao, Xu, Weiming, Xu, Liangliang, Zhang, Peng
Formato:	Preprint
Publicado:	2025
Materias:	Machine Learning
Acceso en línea:	Acceder al recurso
Etiquetas:	Agregar Etiqueta Sin Etiquetas, Sea el primero en etiquetar este registro!

Ejemplares similares

Dynamical Mode Recognition of Coupled Flame Oscillators by Supervised and Unsupervised Learning Approaches
por: Xu, Weiming, et al.
Publicado: (2024)

Dimensionality Reduction and Dynamical Mode Recognition of Circular Arrays of Flame Oscillators Using Deep Neural Network
por: Xu, Weiming, et al.
Publicado: (2023)

Stabilization Analysis and Mode Recognition of Kerosene Supersonic Combustion: A Deep Learning Approach Based on Res-CNN-beta-VAE
por: Xu, Weiming, et al.
Publicado: (2025)

Combustion Condition Identification using a Decision Tree based Machine Learning Algorithm Applied to a Model Can Combustor with High Shear Swirl Injector
por: Archhith, PK, et al.
Publicado: (2024)

Data-driven Learning of Probabilistic Model of Binary Droplet Collision for Spray Simulation
por: Xu, Weiming, et al.
Publicado: (2026)

Steam Turbine Anomaly Detection: An Unsupervised Learning Approach Using Enhanced Long Short-Term Memory Variational Autoencoder
por: Xu, Weiming, et al.
Publicado: (2024)

Influence of Centre Body on the Dynamics of Isothermal Flow Swirl Combustor
por: Pathak, Ratnesh, et al.
Publicado: (2025)

Spectral Edge Dynamics Reveal Functional Modes of Learning
por: Xu, Yongzhong
Publicado: (2026)

Hierarchical Multi-Fidelity Learning for Predicting Three-Dimensional Flame Wrinkling and Turbulent Burning Velocity
por: Zolfaghari, Saghar, et al.
Publicado: (2026)

Performance Evaluation of RANS-Based Turbulence Models in Predicting Turbulent Non-Premixed Swirling Combustion within a Realistic Can Combustor
por: Kumar, Aishvarya, et al.
Publicado: (2026)

Understanding Dynamics of Adam in Zero-Sum Games: An ODE Approach
por: Feng, Yi, et al.
Publicado: (2026)

Sensitivity of Isothermal Swirl Combustor Flow to Inlet Reynolds Number
por: Mahato, Madan Lal, et al.
Publicado: (2026)

Multivariate Time-series Anomaly Detection via Dynamic Model Pool & Ensembling
por: Hu, Wei, et al.
Publicado: (2026)

Mixing Signals: Data Augmentation Approach for Deep Learning Based Modulation Recognition
por: Xu, Xinjie, et al.
Publicado: (2022)

Efficient Neural Hybrid System Learning and Transition System Abstraction for Dynamical Systems
por: Yang, Yejiang, et al.
Publicado: (2024)

Dynamic Modes as Time Representation for Spatiotemporal Forecasting
por: Kong, Menglin, et al.
Publicado: (2025)

A Machine Learning-Based Multimodal Framework for Wearable Sensor-Based Archery Action Recognition and Stress Estimation
por: Liu, Xianghe, et al.
Publicado: (2025)

REBAR: Retrieval-Based Reconstruction for Time-series Contrastive Learning
por: Xu, Maxwell A., et al.
Publicado: (2023)

UniDyG: A Unified and Effective Representation Learning Approach for Large Dynamic Graphs
por: Xu, Yuanyuan, et al.
Publicado: (2025)

CORAL: Concept Drift Representation Learning for Co-evolving Time-series
por: Xu, Kunpeng, et al.
Publicado: (2025)

Learning Dynamic Graph Representations through Timespan View Contrasts
por: Xu, Yiming, et al.
Publicado: (2026)

A Test-Time Learning Approach to Reparameterize the Geophysical Inverse Problem with a Convolutional Neural Network
por: Xu, Anran, et al.
Publicado: (2023)

Session-Level Dynamic Ad Load Optimization using Offline Robust Reinforcement Learning
por: Liu, Tao, et al.
Publicado: (2025)

ESA: Example Sieve Approach for Multi-Positive and Unlabeled Learning
por: Li, Zhongnian, et al.
Publicado: (2024)

How Learning Dynamics Drive Adversarially Robust Generalization?
por: Xu, Yuelin, et al.
Publicado: (2024)

Learning to Represent Surroundings, Anticipate Motion and Take Informed Actions in Unstructured Environments
por: Zhi, Weiming
Publicado: (2024)

GTM: A General Time-series Model for Enhanced Representation Learning of Time-Series Data
por: He, Cheng, et al.
Publicado: (2025)

Towards Accurate and Interpretable Time-series Forecasting: A Polynomial Learning Approach
por: Liu, Bo, et al.
Publicado: (2026)

Long-Tailed Recognition via Information-Preservable Two-Stage Learning
por: Lin, Fudong, et al.
Publicado: (2025)

Parametric Learning of Time-Advancement Operators for Unstable Flame Evolution
por: Yu, Rixin, et al.
Publicado: (2024)

Accelerating Quantum Reinforcement Learning with a Quantum Natural Policy Gradient Based Approach
por: Xu, Yang, et al.
Publicado: (2025)

A Lightweight Sparse Interaction Network for Time Series Forecasting
por: Zhang, Xu, et al.
Publicado: (2026)

Distributed Dynamic Invariant Causal Prediction in Environmental Time Series
por: Hao, Ziruo, et al.
Publicado: (2026)

Multi-modal Dynamic Proxy Learning for Personalized Multiple Clustering
por: Xu, Jinfeng, et al.
Publicado: (2025)

Free-Space Optical Channel Turbulence Prediction: A Machine Learning Approach
por: Islam, Md Zobaer, et al.
Publicado: (2024)

Stochastic Fractional Neural Operators: A Symmetrized Approach to Modeling Turbulence in Complex Fluid Dynamics
por: Santos, Rômulo Damasclin Chaves dos, et al.
Publicado: (2025)

Deep Reinforcement Learning for Solving Management Problems: Towards A Large Management Mode
por: Jiang, Jinyang, et al.
Publicado: (2024)

Amortized Predictability-aware Training Framework for Time Series Forecasting and Classification
por: Zhang, Xu, et al.
Publicado: (2026)

Sublinear-Time Algorithms for Diagonally Dominant Systems and Applications to the Friedkin-Johnsen Model
por: Feng, Weiming, et al.
Publicado: (2025)

Online Kernel Dynamic Mode Decomposition for Streaming Time Series Forecasting with Adaptive Windowing
por: Salazar, Christopher, et al.
Publicado: (2025)