Copertina

Learning Low-Dimensional Quadratic-Embeddings of High-Fidelity Nonlinear Dynamics using Deep Learning

Fuente: arXiv

Salvato in:

Dettagli Bibliografici
Autori principali:	Goyal, Pawan, Benner, Peter
Natura:	Preprint
Pubblicazione:	2021
Soggetti:	Machine Learning Systems and Control
Accesso online:	Acceder al recurso
Tags:	Aggiungi Tag Nessun Tag, puoi essere il primo ad aggiungerne!!

Documenti analoghi

Generalized Quadratic Embeddings for Nonlinear Dynamics using Deep Learning
di: Goyal, Pawan, et al.
Pubblicazione: (2022)

Stability-Certified Learning of Control Systems with Quadratic Nonlinearities
di: Duff, Igor Pontes, et al.
Pubblicazione: (2024)

Guaranteed Stable Quadratic Models and their applications in SINDy and Operator Inference
di: Goyal, Pawan, et al.
Pubblicazione: (2023)

Learning Low-Dimensional Embeddings for Black-Box Optimization
di: Busetto, Riccardo, et al.
Pubblicazione: (2025)

Data-Driven Identification of Quadratic Representations for Nonlinear Hamiltonian Systems using Weakly Symplectic Liftings
di: Yildiz, Süleyman, et al.
Pubblicazione: (2023)

GN-SINDy: Greedy Sampling Neural Network in Sparse Identification of Nonlinear Partial Differential Equations
di: Forootani, Ali, et al.
Pubblicazione: (2024)

Dynamic Importance Learning using Fisher Information Matrix (FIM) for Nonlinear Dynamic Mapping
di: Eivaghi, Vahid MohammadZadeh, et al.
Pubblicazione: (2024)

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

Deep Learning of Dynamic Systems using System Identification Toolbox(TM)
di: Dai, Tianyu, et al.
Pubblicazione: (2024)

Learning Deep Dissipative Dynamics
di: Okamoto, Yuji, et al.
Pubblicazione: (2024)

Scalar Federated Learning for Linear Quadratic Regulator
di: Rostami, Mohammadreza, et al.
Pubblicazione: (2026)

Nonlinear System Identification of Swarm of UAVs Using Deep Learning Methods
di: Yazdannik, Saman, et al.
Pubblicazione: (2023)

High Effort, Low Gain: Fundamental Limits of Active Learning for Linear Dynamical Systems
di: Chatzikiriakos, Nicolas, et al.
Pubblicazione: (2025)

Learning Decentralized Linear Quadratic Regulators with $\sqrt{T}$ Regret
di: Ye, Lintao, et al.
Pubblicazione: (2022)

Iterative Learning Control of Fast, Nonlinear, Oscillatory Dynamics (Preprint)
di: Brooks, John W., et al.
Pubblicazione: (2024)

Learning Exactly Linearizable Deep Dynamics Models
di: Moriyasu, Ryuta, et al.
Pubblicazione: (2023)

Improving Low-Fidelity Models of Li-ion Batteries via Hybrid Sparse Identification of Nonlinear Dynamics
di: da Silva, Samuel Filgueira, et al.
Pubblicazione: (2024)

Learning Accurate Extended-Horizon Predictions of High Dimensional Trajectories
di: Gaudet, Brian, et al.
Pubblicazione: (2019)

A Physics-informed Machine Learning-based Control Method for Nonlinear Dynamic Systems with Highly Noisy Measurements
di: Ma, Mason, et al.
Pubblicazione: (2023)

Universal Learning of Stochastic Dynamics for Exact Belief Propagation using Bernstein Normalizing Flows
di: Amorese, Peter, et al.
Pubblicazione: (2025)

Steady-State Error Compensation for Reinforcement Learning with Quadratic Rewards
di: Wang, Liyao, et al.
Pubblicazione: (2024)

A Data-Integrated Framework for Learning Fractional-Order Nonlinear Dynamical Systems
di: Yaghooti, Bahram, et al.
Pubblicazione: (2025)

Learning Subsystem Dynamics in Nonlinear Systems via Port-Hamiltonian Neural Networks
di: van Otterdijk, G. J. E., et al.
Pubblicazione: (2024)

Sample Complexity of the Linear Quadratic Regulator: A Reinforcement Learning Lens
di: Moghaddam, Amirreza Neshaei, et al.
Pubblicazione: (2024)

The Wisdom of the Crowd: High-Fidelity Classification of Cyber-Attacks and Faults in Power Systems Using Ensemble and Machine Learning
di: Abukhousa, Emad, et al.
Pubblicazione: (2025)

Learning reduced-order Quadratic-Linear models in Process Engineering using Operator Inference
di: Gosea, Ion Victor, et al.
Pubblicazione: (2024)

Cost-Driven Representation Learning for Linear Quadratic Gaussian Control: Part I
di: Tian, Yi, et al.
Pubblicazione: (2022)

Cost-Driven Representation Learning for Linear Quadratic Gaussian Control: Part II
di: Tian, Yi, et al.
Pubblicazione: (2026)

Model-Free Learning for the Linear Quadratic Regulator over Rate-Limited Channels
di: Ye, Lintao, et al.
Pubblicazione: (2024)

Foundations of Safe Online Reinforcement Learning in the Linear Quadratic Regulator: Generalized Baselines
di: Schiffer, Benjamin, et al.
Pubblicazione: (2024)

Foundations of Safe Online Reinforcement Learning in the Linear Quadratic Regulator: $\sqrt{T}$-Regret
di: Schiffer, Benjamin, et al.
Pubblicazione: (2025)

Regret Analysis of Multi-task Representation Learning for Linear-Quadratic Adaptive Control
di: Lee, Bruce D., et al.
Pubblicazione: (2024)

Approximate Thompson Sampling for Learning Linear Quadratic Regulators with $O(\sqrt{T})$ Regret
di: Kim, Yeoneung, et al.
Pubblicazione: (2024)

Learning to Boost the Performance of Stable Nonlinear Systems
di: Furieri, Luca, et al.
Pubblicazione: (2024)

High-Dimensional Surrogate Modeling for Closed-Loop Learning of Neural-Network-Parameterized Model Predictive Control
di: Hirt, Sebastian, et al.
Pubblicazione: (2025)

Optimizing Falsification for Learning-Based Control Systems: A Multi-Fidelity Bayesian Approach
di: Shahrooei, Zahra, et al.
Pubblicazione: (2024)

Model-Agnostic Zeroth-Order Policy Optimization for Meta-Learning of Ergodic Linear Quadratic Regulators
di: Pan, Yunian, et al.
Pubblicazione: (2024)

Hybrid Meta-Learning Framework for Anomaly Forecasting in Nonlinear Dynamical Systems via Physics-Inspired Simulation and Deep Ensembles
di: Bereketoglu, Abdullah Burkan
Pubblicazione: (2025)

Learning from Demonstration with Implicit Nonlinear Dynamics Models
di: Fagan, Peter David, et al.
Pubblicazione: (2024)

Active Learning for Control-Oriented Identification of Nonlinear Systems
di: Lee, Bruce D., et al.
Pubblicazione: (2024)