Cover Image

Koopman Theory-Inspired Method for Learning Time Advancement Operators in Unstable Flame Front Evolution

Fuente: arXiv

Saved in:

Bibliographic Details
Main Authors:	Yu, Rixin, Herbert, Marco, Klein, Markus, Hodzic, Erdzan
Format:	Preprint
Published:	2024
Subjects:	Dynamical Systems Machine Learning Mathematical Physics
Online Access:	Acceder al recurso
Tags:	Add Tag No Tags, Be the first to tag this record!

Similar Items

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

Learning Flame Evolution Operator under Hybrid Darrieus Landau and Diffusive Thermal Instability
by: Yu, Rixin, et al.
Published: (2024)

Kernel Methods for the Approximation of the Eigenfunctions of the Koopman Operator
by: Lee, Jonghyeon, et al.
Published: (2024)

Closed-Loop Koopman Operator Approximation
by: Dahdah, Steven, et al.
Published: (2023)

Learning Spatio-Temporal Dynamics via Operator-Valued RKHS and Kernel Koopman Methods
by: Withanachchi, Mahishanka
Published: (2025)

Koopman Learning with Episodic Memory
by: Redman, William T., et al.
Published: (2023)

Uncertainty Modelling and Robust Observer Synthesis using the Koopman Operator
by: Dahdah, Steven, et al.
Published: (2024)

Generative Modeling through Koopman Spectral Analysis: An Operator-Theoretic Perspective
by: Xu, Yuanchao, et al.
Published: (2025)

Effective Dynamics and Transition Pathways from Koopman-Inspired Neural Learning of Collective Variables
by: Sikorski, Alexander, et al.
Published: (2026)

Koopman Operator Identification of Model Parameter Trajectories for Temporal Domain Generalization (KOMET)
by: Hoover, Randy C., et al.
Published: (2026)

Learning Nonholonomic Dynamics with Constraint Discovery
by: Wang, Baiyue, et al.
Published: (2024)

Deep Learning for Koopman Operator Estimation in Idealized Atmospheric Dynamics
by: Millard, David, et al.
Published: (2024)

Self-Supervised Evolution Operator Learning for High-Dimensional Dynamical Systems
by: Turri, Giacomo, et al.
Published: (2025)

Efficient Parametric SVD of Koopman Operator for Stochastic Dynamical Systems
by: Jeong, Minchan, et al.
Published: (2025)

Koopman Kernel Regression
by: Bevanda, Petar, et al.
Published: (2023)

Deep Learning of the Evolution Operator Enables Forecasting of Out-of-Training Dynamics in Chaotic Systems
by: Shokar, Ira J. S., et al.
Published: (2025)

Learning Koopman-based Stability Certificates for Unknown Nonlinear Systems
by: Zhou, Ruikun, et al.
Published: (2024)

Trustworthy Koopman Operator Learning: Invariance Diagnostics and Error Bounds
by: Conradie, Gustav, et al.
Published: (2026)

An Inverse Scattering Inspired Fourier Neural Operator for Time-Dependent PDE Learning
by: Yu, Rixin
Published: (2025)

ResKoopNet: Learning Koopman Representations for Complex Dynamics with Spectral Residuals
by: Xu, Yuanchao, et al.
Published: (2025)

Mori-Zwanzig latent space Koopman closure for nonlinear autoencoder
by: Gupta, Priyam, et al.
Published: (2023)

Leveraging KANs For Enhanced Deep Koopman Operator Discovery
by: Nehma, George, et al.
Published: (2024)

Data-driven Modeling of Granular Chains with Modern Koopman Theory
by: Parsa, Atoosa, et al.
Published: (2024)

Dynamical systems and complex networks: A Koopman operator perspective
by: Klus, Stefan, et al.
Published: (2024)

Model-Based Reinforcement Learning Control of Reaction-Diffusion Problems
by: Schenk, Christina, et al.
Published: (2024)

Compression of the Koopman matrix for nonlinear physical models via hierarchical clustering
by: Nishikata, Tomoya, et al.
Published: (2024)

Equivariance and partial observations in Koopman operator theory for partial differential equations
by: Peitz, Sebastian, et al.
Published: (2023)

Kernel Methods for Some Transport Equations with Application to Learning Kernels for the Approximation of Koopman Eigenfunctions: A Unified Approach via Variational Methods, Green's Functions and the Method of Characteristics
by: Hamzi, Boumediene, et al.
Published: (2026)

On Data-Driven Koopman Representations of Nonlinear Delay Differential Equations
by: Rajkumar, Santosh Mohan, et al.
Published: (2026)

The Mean-Field Dynamics of Transformers
by: Rigollet, Philippe
Published: (2025)

A Dynamics-Informed Gaussian Process Framework for 2D Stochastic Navier-Stokes via Quasi-Gaussianity
by: Hamzi, Boumediene, et al.
Published: (2025)

Koopman-based surrogate modeling for reinforcement-learning-control of Rayleigh-Benard convection
by: Plotzki, Tim, et al.
Published: (2026)

Koopman-Assisted Reinforcement Learning
by: Rozwood, Preston, et al.
Published: (2024)

Automatic feature identification in least-squares policy iteration using the Koopman operator framework
by: Zagabe, Christian Mugisho, et al.
Published: (2026)

Data-driven Nonlinear Model Reduction using Koopman Theory: Integrated Control Form and NMPC Case Study
by: Schulze, Jan C., et al.
Published: (2024)

Kernel Methods for Stochastic Dynamical Systems with Application to Koopman Eigenfunctions: Feynman-Kac Representations and RKHS Approximation
by: Hamzi, Boumediene, et al.
Published: (2026)

Bilinear Input Modulation for Mamba: Koopman Bilinear Forms for Memory Retention and Multiplicative Computation
by: Fujii, Hiroki, et al.
Published: (2026)

On the algebra of Koopman eigenfunctions and on some of their infinities
by: Monfared, Zahra, et al.
Published: (2026)

On the Periodic Orbits of the Dual Logarithmic Derivative Operator
by: Yu, Xiaohang, et al.
Published: (2025)

Weak Generative Sampler to Efficiently Sample Invariant Distribution of Stochastic Differential Equation
by: Cai, Zhiqiang, et al.
Published: (2024)