Muhammad Zakwan

Muhammad Zakwan

PhD Student

EPF Lausanne

Realizing mathematical constructions to tangible real-world applications for a better future.

Muhammad Zakwan is a doctoral assistant in the Dependable Control and Decision group (DECODE) at École Polytechnique Fédérale de Lausanne (EPFL). He is also a member of the National Centre of Competence in Research (NCCR) Automation. Zakwan holds an Electrical and Electronics Engineering degree from Bilkent University (Turkey) and a Bachelor of Science in Electrical Engineering from the Pakistan Institute of Engineering and Applied Sciences (Pakistan). His research interests include LPV systems, hybrid dynamical systems, time-delay systems, distributed systems, and machine learning.

Scientific Publications

Published

Stable Linear Subspace Identification: A Machine Learning Approach

ArXiv

Loris DiNatale, Muhammad Zakwan, Bratislav Svetozarevic, Philipp Heer, Giancarlo Ferrari Trecate, Colin Jones

2023

Doi.org Collection

Published

Universal Approximation Property of Hamiltonian Deep Neural Networks

IEEE Control System Letters

Vol 7 Pages 2689-2694

Muhammad Zakwan, Massimiliano d'Angelo, Giancarlo Ferrari Trecate

2023

Collection

Published

Physically Consistent Neural ODEs for Learning Multi-Physics Systems

Proceedings of IFAC World congress 2023

Muhammad Zakwan, Loris DiNatale, Bratislav Svetozarevic, Philipp Heer, Colin Jones, Giancarlo Ferrari Trecate

2022

Doi.org Arxiv.org

Published

Robust Classification Using Contractive Hamiltonian Neural ODEs

IEEE Control Systems Letters

Vol 7 Pages 145-150

Muhammad Zakwan, Liang Xu, Giancarlo Ferrari Trecate

2022

Doi.org Collection

Published

Distributed Neural Network Control with Dependability Guarantees: a Compositional Port-Hamiltonian Approach

Proceedings of The 4th Annual Learning for Dynamics and Control Conference

Vol 168 Pages 571-583

Luca Furieri, Clara Lucía Galimberti, Muhammad Zakwan, Giancarlo Ferrari Trecate

2022

Doi.org Collection

Published

Neural Energy Casimir Control for Port-Hamiltonian Systems

arXiv

Liang Xu, Muhammad Zakwan, Giancarlo Ferrari Trecate

2021

Doi.org Collection

Research projects as Researcher

Title

Principal Investigators

ODE networks for data-based control

Giancarlo Ferrari Trecate

Summary

In spite of several success stories, standard deep networks can lead to unstable forward propagation or suffer from an ill-posed learning process, which might result in vulnerability to adversarial attacks. In this project we will study deep network architectures based on ODEs for mitigating these problems. Furthermore, we will investigate the use of these networks for parametrizing regulators that can be tuned from available data. The new methods will be tested for the control of inverters, smart loads and microgrid clusters, which are relevant for the energy systems considered in the NCCR.

People involved