Related references
Note: Only part of the references are listed.Parsimony as the ultimate regularizer for physics-informed machine learning
J. Nathan Kutz et al.
NONLINEAR DYNAMICS (2022)
Rapid identification of switched systems: A data-driven method in variational framework
ChunJiang Li et al.
SCIENCE CHINA-TECHNOLOGICAL SCIENCES (2021)
Machine learning based digital twin for dynamical systems with multiple time-scales
S. Chakraborty et al.
COMPUTERS & STRUCTURES (2021)
Forecasting Hamiltonian dynamics without canonical coordinates
Anshul Choudhary et al.
NONLINEAR DYNAMICS (2021)
Bio-Inspired Vibration Isolation: Methodology and Design
Ge Yan et al.
APPLIED MECHANICS REVIEWS (2021)
A bi-bandwidth extended state observer for a system with measurement noise and its application to aircraft with abrupt structural damage
Lingyu Yang et al.
AEROSPACE SCIENCE AND TECHNOLOGY (2021)
Data-Driven Aerospace Engineering: Reframing the Industry with Machine Learning
Steven L. Brunton et al.
AIAA JOURNAL (2021)
Safe and Efficient Model-free Adaptive Control via Bayesian Optimization
Christopher Konig et al.
2021 IEEE INTERNATIONAL CONFERENCE ON ROBOTICS AND AUTOMATION (ICRA 2021) (2021)
The digital twin of discrete dynamic systems: Initial approaches and future challenges
R. Ganguli et al.
APPLIED MATHEMATICAL MODELLING (2020)
Data-driven automated discovery of variational laws hidden in physical systems
Zhilong Huang et al.
JOURNAL OF THE MECHANICS AND PHYSICS OF SOLIDS (2020)
Digital Twins: State-of-the-Art and Future Directions for Modeling and Simulation in Engineering Dynamics Applications
D. J. Wagg et al.
ASCE-ASME JOURNAL OF RISK AND UNCERTAINTY IN ENGINEERING SYSTEMS PART B-MECHANICAL ENGINEERING (2020)
On Digital Twins, Mirrors, and Virtualizations: Frameworks for Model Verification and Validation
K. Worden et al.
ASCE-ASME JOURNAL OF RISK AND UNCERTAINTY IN ENGINEERING SYSTEMS PART B-MECHANICAL ENGINEERING (2020)
Using noisy or incomplete data to discover models of spatiotemporal dynamics
Patrick A. K. Reinbold et al.
PHYSICAL REVIEW E (2020)
Stochastic Averaging-Based Direct Method for Response Control of Nonlinear Vibrating System
Ying Yang et al.
JOURNAL OF ENGINEERING MECHANICS (2019)
Variational system identification of the partial differential equations governing the physics of pattern-formation: Inference under varying fidelity and noise
Z. Wang et al.
COMPUTER METHODS IN APPLIED MECHANICS AND ENGINEERING (2019)
Semi-supervised structural linear/nonlinear damage detection and characterization using sparse identification
Zhilu Lai et al.
STRUCTURAL CONTROL & HEALTH MONITORING (2019)
Synchronization control of oscillator networks using symbolic regression
Julien Gout et al.
NONLINEAR DYNAMICS (2018)
Modeling and harnessing sparse and low-rank data structure: a new paradigm for structural dynamics, identification, damage detection, and health monitoring
Satish Nagarajaiah et al.
STRUCTURAL CONTROL & HEALTH MONITORING (2017)
Sparse model selection via integral terms
Hayden Schaeffer et al.
PHYSICAL REVIEW E (2017)
Discovering governing equations from data by sparse identification of nonlinear dynamical systems
Steven L. Brunton et al.
PROCEEDINGS OF THE NATIONAL ACADEMY OF SCIENCES OF THE UNITED STATES OF AMERICA (2016)
An Introduction to Optimal Control
Carlo Cossu
APPLIED MECHANICS REVIEWS (2014)
From model-based control to data-driven control: Survey, classification and perspective
Zhong-Sheng Hou et al.
INFORMATION SCIENCES (2013)
Model-free control
Michel Fliess et al.
INTERNATIONAL JOURNAL OF CONTROL (2013)
Distilling Free-Form Natural Laws from Experimental Data
Michael Schmidt et al.
SCIENCE (2009)
Automated reverse engineering of nonlinear dynamical systems
Josh Bongard et al.
PROCEEDINGS OF THE NATIONAL ACADEMY OF SCIENCES OF THE UNITED STATES OF AMERICA (2007)