Copertina

Finding separatrices of dynamical flows with Deep Koopman Eigenfunctions

Fuente: arXiv

Salvato in:

Dettagli Bibliografici
Autori principali:	Dabholkar, Kabir V., Barak, Omri
Natura:	Preprint
Pubblicazione:	2025
Soggetti:	Machine Learning
Accesso online:	Acceder al recurso
Tags:	Aggiungi Tag Nessun Tag, puoi essere il primo ad aggiungerne!!

Documenti analoghi

When predict can also explain: few-shot prediction to select better neural latents
di: Dabholkar, Kabir, et al.
Pubblicazione: (2024)

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

Prediction horizon shapes representations in predictive learning
di: Ratzon, Aviv, et al.
Pubblicazione: (2025)

Koopman Eigenfunction-Based Identification and Optimal Nonlinear Control of Turbojet Engine
di: Grasev, David
Pubblicazione: (2025)

Learning Dynamics of RNNs in Closed-Loop Environments
di: Ger, Yoav, et al.
Pubblicazione: (2025)

Learning reveals invisible structure in low-rank RNNs
di: Ger, Yoav, et al.
Pubblicazione: (2026)

Rigged Dynamic Mode Decomposition: Data-Driven Generalized Eigenfunction Decompositions for Koopman Operators
di: Colbrook, Matthew J., et al.
Pubblicazione: (2024)

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

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

Generative Modeling of Regular and Irregular Time Series Data via Koopman VAEs
di: Naiman, Ilan, et al.
Pubblicazione: (2023)

Eigenfunction Extraction for Ordered Representation Learning
di: Varıcı, Burak, et al.
Pubblicazione: (2025)

One-Step Offline Distillation of Diffusion-based Models via Koopman Modeling
di: Berman, Nimrod, et al.
Pubblicazione: (2025)

Analyzing Deep Transformer Models for Time Series Forecasting via Manifold Learning
di: Kaufman, Ilya, et al.
Pubblicazione: (2024)

Conformal Online Learning of Deep Koopman Linear Embeddings
di: Gao, Ben, et al.
Pubblicazione: (2025)

Koopman Regularized Deep Speech Disentanglement for Speaker Verification
di: Chazaridis, Nikos, et al.
Pubblicazione: (2026)

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
di: Hamzi, Boumediene, et al.
Pubblicazione: (2026)

CarSpeedNet: Learning-Based Speed Estimation from Accelerometer-Only Inertial Sensing
di: Or, Barak
Pubblicazione: (2024)

Automatic Stability and Recovery for Neural Network Training
di: Or, Barak
Pubblicazione: (2026)

Kalman-Inspired Runtime Stability and Recovery in Hybrid Reasoning Systems
di: Or, Barak
Pubblicazione: (2026)

Koopman-based Deep Learning for Nonlinear System Estimation
di: Sun, Zexin, et al.
Pubblicazione: (2024)

Spatially Aware Dictionary-Free Eigenfunction Identification for Modeling and Control of Nonlinear Dynamical Systems
di: Grasev, David
Pubblicazione: (2025)

On the Koopman-Based Generalization Bounds for Multi-Task Deep Learning
di: Mohammadigohari, Mahdi, et al.
Pubblicazione: (2025)

A Schrödinger Eigenfunction Method for Long-Horizon Stochastic Optimal Control
di: Claeys, Louis, et al.
Pubblicazione: (2026)

Imitation Learning with Limited Actions via Diffusion Planners and Deep Koopman Controllers
di: Bi, Jianxin, et al.
Pubblicazione: (2024)

Koopman-Based Generalization of Deep Reinforcement Learning With Application to Wireless Communications
di: Termehchi, Atefeh, et al.
Pubblicazione: (2025)

Deep Koopman-based Control of Quality Variation in Multistage Manufacturing Systems
di: Chen, Zhiyi, et al.
Pubblicazione: (2024)

Finding geodesics with the Deep Ritz method
di: Rowan, Conor
Pubblicazione: (2025)

Recent Trends in Modelling the Continuous Time Series using Deep Learning: A Survey
di: Habiba, Mansura, et al.
Pubblicazione: (2024)

DeepKoopFormer: A Koopman Enhanced Transformer Based Architecture for Time Series Forecasting
di: Forootani, Ali, et al.
Pubblicazione: (2025)

XCTFormer: Leveraging Cross-Channel and Cross-Time Dependencies for Enhanced Time-Series Analysis
di: Zexer, Israel, et al.
Pubblicazione: (2026)

First-Order Manifold Data Augmentation for Regression Learning
di: Kaufman, Ilya, et al.
Pubblicazione: (2024)

Physics-informed Blind Reconstruction of Dense Fields from Sparse Measurements using Neural Networks with a Differentiable Simulator
di: Aloni, Ofek, et al.
Pubblicazione: (2026)

Shortcut Features as Top Eigenfunctions of NTK: A Linear Neural Network Case and More
di: Lim, Jinwoo, et al.
Pubblicazione: (2026)

Stochastic Deep Koopman Model for Quality Propagation Analysis in Multistage Manufacturing Systems
di: Chen, Zhiyi, et al.
Pubblicazione: (2023)

Neural Koopman prior for data assimilation
di: Frion, Anthony, et al.
Pubblicazione: (2023)

NeuroKoopman Dynamic Causal Discovery
di: Adesunkanmi, Rahmat, et al.
Pubblicazione: (2024)

Glocal Hypergradient Estimation with Koopman Operator
di: Hataya, Ryuichiro, et al.
Pubblicazione: (2024)

Curvature Enhanced Data Augmentation for Regression
di: Sirot, Ilya Kaufman, et al.
Pubblicazione: (2025)

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

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