Imagen de Portada

Integrated utilization of equations and small dataset in the Koopman operator: applications to forward and inverse problems

Fuente: arXiv

Guardado en:

Detalles Bibliográficos
Autores principales:	Ohta, Ichiro, Koyanagi, Shota, Kinjo, Kayo, Ohkubo, Jun
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

Extraction of nonlinearity in neural networks with Koopman operator
por: Sugishita, Naoki, et al.
Publicado: (2024)

Koopman operator-based discussion on partial observation in stochastic systems
por: Ohkubo, Jun
Publicado: (2025)

Learning Koopman operators for coupled systems via information on governing equations of subsystems
por: Naoi, Tatsuya, et al.
Publicado: (2026)

Tensor-based computation of the Koopman generator via operator logarithm
por: Kishimoto, Tatsuya, et al.
Publicado: (2026)

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

Improvement of system identification of stochastic systems via Koopman generator and locally weighted expectation
por: Tahara, Yuki, et al.
Publicado: (2024)

An invertible generative model for forward and inverse problems
por: van Leeuwen, Tristan, et al.
Publicado: (2025)

Permutation of Tensor-Train Cores for Computing Moments on Stochastic Differential Equations
por: Kinjo, Kayo, et al.
Publicado: (2025)

Weak neural variational inference for solving Bayesian inverse problems without forward models: applications in elastography
por: Scholz, Vincent C., et al.
Publicado: (2024)

Neural network initialization with nonlinear characteristics and information on hierarchical features
por: Homma, Hikaru, et al.
Publicado: (2025)

Extraction of linearized models from pre-trained networks via knowledge distillation
por: Kimura, Fumito, et al.
Publicado: (2026)

Aspects of importance sampling in parameter selection for neural networks using ridgelet transform
por: Homma, Hikaru, et al.
Publicado: (2024)

Invertible Koopman neural operator for data-driven modeling of partial differential equations
por: Jin, Yuhong, et al.
Publicado: (2025)

Learning truly monotone operators with applications to nonlinear inverse problems
por: Belkouchi, Younes, et al.
Publicado: (2024)

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

Robust Counterfactual Explanations under Model Multiplicity Using Multi-Objective Optimization
por: Kinjo, Keita
Publicado: (2025)

Deep learning methods for inverse problems using connections between proximal operators and Hamilton-Jacobi equations
por: Akande, Oluwatosin, et al.
Publicado: (2025)

Scalable physics-informed deep generative model for solving forward and inverse stochastic differential equations
por: Zhou, Shaoqian, et al.
Publicado: (2025)

Conditionally adaptive augmented Lagrangian method for physics-informed learning of forward and inverse problems
por: Hu, Qifeng, et al.
Publicado: (2025)

FlamePINN-1D: Physics-informed neural networks to solve forward and inverse problems of 1D laminar flames
por: Wu, Jiahao, et al.
Publicado: (2024)

Koopman operator for time-dependent reliability analysis
por: N., Navaneeth, et al.
Publicado: (2022)

Reliable and efficient inverse analysis using physics-informed neural networks with normalized distance functions and adaptive weight tuning
por: Deguchi, Shota, et al.
Publicado: (2025)

Fourier neural operator for learning solutions to macroscopic traffic flow models: Application to the forward and inverse problems
por: Thodi, Bilal Thonnam, et al.
Publicado: (2023)

A unified physics-informed generative operator framework for general inverse problems
por: Bao, Gang, et al.
Publicado: (2025)

Gaussian process policy iteration with additive Schwarz acceleration for forward and inverse HJB and mean field game problems
por: Yang, Xianjin, et al.
Publicado: (2025)

Deep Koopman operator framework for causal discovery in nonlinear dynamical systems
por: Nathaniel, Juan, et al.
Publicado: (2025)

Adaptive operator learning for infinite-dimensional Bayesian inverse problems
por: Gao, Zhiwei, et al.
Publicado: (2023)

On the relationship between Koopman operator approximations and neural ordinary differential equations for data-driven time-evolution predictions
por: Buzhardt, Jake, et al.
Publicado: (2024)

Gaussian processes for Bayesian inverse problems associated with linear partial differential equations
por: Bai, Tianming, et al.
Publicado: (2023)

Characterization of Locality in Spin States and Forced Moves for Optimizations
por: Sato, Yoshiki, et al.
Publicado: (2023)

Attention-Enhanced Reservoir Computing
por: Köster, Felix, et al.
Publicado: (2023)

Balanced Neural ODEs: nonlinear model order reduction and Koopman operator approximations
por: Aka, Julius, et al.
Publicado: (2024)

Advancing accelerator virtual beam diagnostics through latent evolution modeling: an integrated solution to forward, inverse, tuning, and UQ problems
por: Rautela, Mahindra, et al.
Publicado: (2026)

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

RED-DiffEq: Regularization by denoising diffusion models for solving inverse PDE problems with application to full waveform inversion
por: Shan, Siming, et al.
Publicado: (2025)

Diffusion models for inverse problems
por: Chung, Hyungjin, et al.
Publicado: (2025)

Koopman analysis of combinatorial optimization problems with replica exchange Monte Carlo method
por: Naoi, Tatsuya, et al.
Publicado: (2024)

Learning Hamiltonian neural Koopman operator and simultaneously sustaining and discovering conservation law
por: Zhang, Jingdong, et al.
Publicado: (2024)

Kolmogorov Arnold Informed neural network: A physics-informed deep learning framework for solving forward and inverse problems based on Kolmogorov Arnold Networks
por: Wang, Yizheng, et al.
Publicado: (2024)

Ricci flow regularization in latent spaces for the forward learning of partial differential equations
por: Gracyk, Andrew
Publicado: (2024)