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dynoNet: a neural network architecture for learning dynamical systems

International Journal of Adaptive Control and Signal Processing (IJACSP), 2020

3 June 2020

Papers citing "dynoNet: a neural network architecture for learning dynamical systems"

24 / 24 papers shown

Bayesian Inference and Learning in Nonlinear Dynamical Systems: A Framework for Incorporating Explicit and Implicit Prior Knowledge

236

21 Aug 2025

Efficient identification of linear, parameter-varying, and nonlinear systems with noise models

Alberto Bemporad

Roland Tóth

220

16 Apr 2025

Meta-Learning for Physically-Constrained Neural System Identification

234

10 Jan 2025

Entropy stable conservative flux form neural networks

257

04 Nov 2024

Enhanced Transformer architecture for in-context learning of dynamical systemsEuropean Control Conference (ECC), 2024

167

04 Oct 2024

Combining Federated Learning and Control: A Survey

381

12 Jul 2024

Differentiable Time-Varying Linear Prediction in the Context of End-to-End Analysis-by-SynthesisInterspeech (Interspeech), 2024

Chin-Yun Yu

Gyorgy Fazekas

294

07 Jun 2024

Baseline Results for Selected Nonlinear System Identification BenchmarksIFAC-PapersOnLine (IFAC-PapersOnLine), 2024

Maarten Schoukens

193

17 May 2024

Differentiable All-pole Filters for Time-varying Audio Systems

Chin-Yun Yu

Christopher Mitcheltree

Alistair Carson

Stefan Bilbao

Joshua D. Reiss

Gyorgy Fazekas

358

11 Apr 2024

Model order reduction of deep structured state-space models: A system-theoretic approach

Marco Forgione

Manas Mejari

Dario Piga

169

21 Mar 2024

Synthetic data generation for system identification: leveraging knowledge transfer from similar systemsIEEE Conference on Decision and Control (CDC), 2024

216

08 Mar 2024

Exploiting the capacity of deep networks only at training stage for nonlinear black-box system identification

V. M. Eivaghi

M. A. Shoorehdeli

384

26 Dec 2023

Structured state-space models are deep Wiener models

194

11 Dec 2023

On the adaptation of in-context learners for system identification

Dario Piga

F. Pura

Marco Forgione

237

07 Dec 2023

From system models to class models: An in-context learning paradigmIEEE Control Systems Letters (L-CSS), 2023

Marco Forgione

F. Pura

Dario Piga

338

25 Aug 2023

Orthogonal Transforms in Neural Networks Amount to Effective Regularization

Krzysztof Zajkac

Wojciech Sopot

Paweł Wachel

190

10 May 2023

Deep networks for system identification: a Survey

372

111

30 Jan 2023

Meta-Learning of Neural State-Space Models Using Data From Similar SystemsIFAC-PapersOnLine (IFAC-PapersOnLine), 2022

Ankush Chakrabarty

Gordon Wichern

C. Laughman

273

14 Nov 2022

Learning neural state-space models: do we need a state estimator?

Marco Forgione

Manas Mejari

Dario Piga

161

26 Jun 2022

Continuous-time identification of dynamic state-space models by deep subspace encodingInternational Conference on Learning Representations (ICLR), 2022

G. Beintema

Maarten Schoukens

R. Tóth

194

20 Apr 2022

On the adaptation of recurrent neural networks for system identification

249

21 Jan 2022

Flexible-Joint Manipulator Trajectory Tracking with Learned Two-Stage Model employing One-Step Future PredictionInternational Conference on Robotic Computing (IRC), 2021

D. Pavlichenko

Sven Behnke

203

06 Dec 2021

Constructing Neural Network-Based Models for Simulating Dynamical SystemsACM Computing Surveys (CSUR), 2021

Christian Møldrup Legaard

329

140

02 Nov 2021

Deep learning with transfer functions: new applications in system identificationIFAC-PapersOnLine (IFAC-PapersOnLine), 2021

Dario Piga

Marco Forgione

Manas Mejari

20 Apr 2021