Likelihood-based generalization of Markov parameter estimation and multiple shooting objectives in system identification

20 December 2022

Nicholas Galioto

Alex Arkady Gorodetsky

Papers citing "Likelihood-based generalization of Markov parameter estimation and multiple shooting objectives in system identification"

23 / 23 papers shown

Title
What Are Bayesian Neural Network Posteriors Really Like? Pavel Izmailov Sharad Vikram Matthew D. Hoffman A. Wilson UQCV BDL 61 382 0 29 Apr 2021
Nonlinear state-space identification using deep encoder networks G. Beintema R. Tóth Maarten Schoukens 15 40 0 14 Dec 2020
Supervised learning from noisy observations: Combining machine-learning techniques with data assimilation Georg Gottwald Sebastian Reich AI4CE 30 63 0 14 Jul 2020
Continuous-time system identification with neural networks: Model structures and fitting criteria Marco Forgione Dario Piga 10 65 0 03 Jun 2020
Logarithmic Regret Bound in Partially Observable Linear Dynamical Systems Sahin Lale Kamyar Azizzadenesheli B. Hassibi Anima Anandkumar 61 95 0 25 Mar 2020
Bayesian System ID: Optimal management of parameter, model, and measurement uncertainty Nicholas Galioto Alex Gorodetsky 43 32 0 04 Mar 2020
Deep regularization and direct training of the inner layers of Neural Networks with Kernel Flows G. Yoo H. Owhadi 34 21 0 19 Feb 2020
Universal Differential Equations for Scientific Machine Learning Christopher Rackauckas Yingbo Ma Julius Martensen Collin Warner K. Zubov R. Supekar Dominic J. Skinner Ali Ramadhan Alan Edelman AI4CE 43 578 0 13 Jan 2020
Symplectic ODE-Net: Learning Hamiltonian Dynamics with Control Yaofeng Desmond Zhong Biswadip Dey Amit Chakraborty PINN 67 270 0 26 Sep 2019
Data-driven approximation of the Koopman generator: Model reduction, system identification, and control Stefan Klus Feliks Nuske Sebastian Peitz Jan-Hendrik Niemann C. Clementi Christof Schütte 38 225 0 23 Sep 2019
Hamiltonian Neural Networks S. Greydanus Misko Dzamba J. Yosinski PINN AI4CE 43 876 0 04 Jun 2019
On the smoothness of nonlinear system identification Antônio H. Ribeiro K. Tiels Jack Umenberger Thomas B. Schon L. A. Aguirre 14 53 0 02 May 2019
Finite Sample Analysis of Stochastic System Identification Anastasios Tsiamis George J. Pappas 21 147 0 21 Mar 2019
Physics Guided RNNs for Modeling Dynamical Systems: A Case Study in Simulating Lake Temperature Profiles X. Jia J. Willard Anuj Karpatne J. Read Jacob Aaron Zwart M. Steinbach Vipin Kumar PINN AI4CE 10 211 0 31 Oct 2018
Neural Ordinary Differential Equations T. Chen Yulia Rubanova J. Bettencourt David Duvenaud AI4CE 193 5,024 0 19 Jun 2018
Non-asymptotic Identification of LTI Systems from a Single Trajectory Samet Oymak N. Ozay 44 222 0 14 Jun 2018
Deep Multi-Output Forecasting: Learning to Accurately Predict Blood Glucose Trajectories Ian Fox Lynn Ang M. Jaiswal R. Pop-Busui Jenna Wiens OOD AI4TS 79 78 0 14 Jun 2018
Essentially No Barriers in Neural Network Energy Landscape Felix Dräxler K. Veschgini M. Salmhofer Fred Hamprecht MoMe 95 430 0 02 Mar 2018
Loss Surfaces, Mode Connectivity, and Fast Ensembling of DNNs T. Garipov Pavel Izmailov Dmitrii Podoprikhin Dmitry Vetrov A. Wilson UQCV 49 746 0 27 Feb 2018
A Family of Iterative Gauss-Newton Shooting Methods for Nonlinear Optimal Control Markus Giftthaler Michael Neunert M. Stäuble J. Buchli Moritz Diehl 34 100 0 29 Nov 2017
PDE-Net: Learning PDEs from Data Zichao Long Yiping Lu Xianzhong Ma Bin Dong DiffM AI4CE 19 750 0 26 Oct 2017
A Bayesian Perspective on Generalization and Stochastic Gradient Descent Samuel L. Smith Quoc V. Le BDL 42 247 0 17 Oct 2017
Regularized linear system identification using atomic, nuclear and kernel-based norms: the role of the stability constraint G. Pillonetto Tianshi Chen A. Chiuso G. Nicolao L. Ljung 58 68 0 02 Jul 2015