Concept
Nonlinear system identification
Related publications (84)
Koopman-based Data-driven Robust Control of Nonlinear Systems Using Integral Quadratic Constraints
This paper introduces a novel method for data-driven robust control of nonlinear systems based on the Koopman operator, utilizing Integral Quadratic Constraints (IQCs). The Koopman operator theory facilitates the linear representation of nonlinear system d ...
2024Explainable Fault Diagnosis of Oil-Immersed Transformers: A Glass-Box Model
Yi Zhang, Wenlong Liao, Zhe Yang
Recently, remarkable progress has been made in the application of machine learning (ML) techniques (e.g., neural networks) to transformer fault diagnosis. However, the diagnostic processes employed by these techniques often suffer from a lack of interpreta ...
Piscataway2024Error assessment of an adaptive finite elements-neural networks method for an elliptic parametric PDE
Marco Picasso, Alexandre Caboussat, Maude Girardin
We present a finite elements-neural network approach for the numerical approximation of parametric partial differential equations. The algorithm generates training data from finite element simulations, and uses a data -driven (supervised) feedforward neura ...
Lausanne2024Unconstrained Parametrization of Dissipative and Contracting Neural Ordinary Differential Equations
Giancarlo Ferrari Trecate, Luca Furieri, Clara Lucía Galimberti, Daniele Martinelli
In this work, we introduce and study a class of Deep Neural Networks (DNNs) in continuous-time. The proposed architecture stems from the combination of Neural Ordinary Differential Equations (Neural ODEs) with the model structure of recently introduced Rec ...
New York2023Backpropagation-free training of deep physical neural networks
Romain Christophe Rémy Fleury, Ali Momeni, Matthieu Francis Malléjac, Babak Rahmani, Marc Philipp Del Hougne
Recent successes in deep learning for vision and natural language processing are attributed to larger models but come with energy consumption and scalability issues. Current training of digital deep-learning models primarily relies on backpropagation that ...
2023Fundamental Limits in Statistical Learning Problems: Block Models and Neural Networks
This thesis focuses on two selected learning problems: 1) statistical inference on graphs models, and, 2) gradient descent on neural networks, with the common objective of defining and analysing the measures that characterize the fundamental limits.In the ...
EPFL2023Physics-Informed Machine Learning for Modeling and Control of Dynamical Systems
Colin Neil Jones, Roland Schwan, Melanie Nicole Zeilinger, Xuan Truong Nghiem
Physics-informed machine learning (PIML) is a set of methods and tools that systematically integrate machine learning (ML) algorithms with physical constraints and abstract mathematical models developed in scientific and engineering domains. As opposed to ...
IEEE2023Closed-loop data-driven modeling and distributed control for islanded microgrids with input constraints
Alireza Karimi, Seyed Sohail Madani, Dongdong Zheng
In this paper a new nonlinear identification method for microgrids based on neural networks is proposed. The system identification process can be done using the available closed-loop system input/output data recorded during normal operation without additio ...
PERGAMON-ELSEVIER SCIENCE LTD2022Local Kernel Regression and Neural Network Approaches to the Conformational Landscapes of Oligopeptides
Michele Ceriotti, Alberto Fabrizio, Benjamin André René Meyer, Edgar Albert Engel, Raimon Fabregat I De Aguilar-Amat, Veronika Juraskova
The application of machine learning to theoretical chemistry has made it possible to combine the accuracy of quantum chemical energetics with the thorough sampling of finite-temperature fluctuations. To reach this goal, a diverse set of methods has been pr ...
2022Control-oriented modeling and analysis of tubular dielectric elastomer actuators dedicated to cardiac assist devices
Yves Perriard, Yoan René Cyrille Civet, Thomas Guillaume Martinez, Armando Matthieu Walter, Ning Liu
This paper deals with the control-oriented modeling of a multilayered dielectric elastomer actuator based tube. The actuator is clamped at both sides and performs a radial displacement. The hyperelastic deformation and the viscoelastic performance, togethe ...
2022