About
The Dynamic Systems Lab (DSL) drives innovation in dynamic systems engineering by integrating mechanical, electrical, and computational technologies. Our mission is to understand, design, and optimise intelligent systems that adapt to real-world complexities.
With a multidisciplinary team, DSL develops cutting-edge solutions that address challenges across industries, including automotive, aerospace, energy, and manufacturing sectors — bridging theory and practice from modelling and simulation to real-time system control and optimisation.
We are committed to fostering collaborations with academic, industrial, and governmental partners, driving impactful projects that propel technological progress and sustainability. Through state-of-the-art facilities and an unwavering dedication to excellence, the DSL is your partner in navigating and mastering the ever-evolving landscape of dynamic engineering and systems innovation.
People
Dr Yukun Hu (Director, Civil, Environmental & Geomatic Eng)
Research Direction: dynamic systems, clean energy & infrastructure
Email: yukun.hu@ucl.ac.uk
Dr Boli Chen (Deputy Director, Electronic & Electrical Eng)
Research Direction: data-driven control, applied mathematics, robotics
Email: boli.chen@ucl.ac.uk
Prof Liz Varga (Environmental & Geomatic Eng)
Research Direction: agent-based methods, complex systems, infrastructure
Email: l.varga@ucl.ac.uk
Prof Sarah Spurgeon (Electronic & Electrical Eng)
Research Direction: control sciences, applied mathematics, robotics
Email: s.spurgeon@ucl.ac.uk
Dr Mehdi Baghdadi (Mechanical Eng)
Research Direction: power electronics, material engineering, battery manufacturing
Email: m.baghdadi@ucl.ac.uk
Dr Peter Ye (Bartlett School of Sustainable Construction)
Research Direction: financial and engineering dynamics, risk modelling, dynamic investment systems
Email: p.ye@ucl.ac.uk
Dr Rhodri Jervis (Chemical Eng)
Research Direction: electrochemical dynamics, degradation and failure, diagnostics & imaging
Email: rhodri.jervis@ucl.ac.uk
Xiaoyuan Cheng
Research Direction: AI for science, operator learning, learning for dynamics and control
Email: ucesxc4@ucl.ac.uk
Yiming Yang
Research Direction: AI for science, statistical learning, weather and climate modelling
Email: zcahyy1@ucl.ac.uk
Yi He
Research Direction: Science Foundation Models, AI for Science, Image Sensing, Time series modelling
Email: yi.he.20@ucl.ac.uk
Dr Sibo Cheng
Research Direction: AI for science, data assimilation, physics-informed ML
Email: sibo.cheng@imperial.ac.uk
Dr Jinhong Wang
Research Direction: computational fluid dynamics, AI for fluids
Email: jinhong.wang17@imperial.ac.uk
Research
The DSL four-pillar research framework integrates Representation, Forecasting, Control, and Optimisation — linking theory and AI methods to model, predict, and optimise dynamic processes across energy, mobility, and industrial applications.
DSL develops mathematical and data-driven representations that capture the intrinsic structure, nonlinearity, and multi-scale behaviour of dynamic systems. Our methods emphasise invariant preservation, interpretable embeddings, and scalable modelling for complex, high-dimensional environments.
DSL forecasting research combines dynamical systems theory with advanced spatio-temporal learning methods — including Koopman operator theory and physics-informed neural networks — to achieve robust, long-horizon prediction and uncertainty quantification in chaotic or partially observed systems.
DSL designs adaptive, safe, and efficient control algorithms using model predictive control (MPC) and reinforcement learning (RL). Our work focuses on the co-design of sensing and control strategies and their real-time implementation in cyber-physical systems for robotics, energy, and mobility networks.
At the macro level, DSL studies multi-agent stochastic optimisation and distributed, game-theoretic, and hierarchical control frameworks. These methods enable coordination, resilience, and efficiency in interconnected dynamic systems spanning energy, transport, and industrial infrastructures.
Experimental Platform
We are developing a confined indoor environment testing platform for autonomous systems. It could simulate the interior of wind turbine blades and post-disaster rescue environments within buildings. This platform supports research in drone and vehicle navigation and control by enabling real-time experiments in a configurable environment with obstacles. It allows for the evaluation of perception, trajectory planning, and collision avoidance strategies under realistic indoor conditions. Testing enquiries and collaboration are welcome.
Publications and Vacancies
Latest news
2026 NCEPU–UCEER: Green Energy Youth Leadership Summer Camp
DSL co-organised the 2026 Green Energy Youth Leadership Summer Camp in Beijing, bringing together students from UK universities to explore green energy, technological innovation and Chinese culture.
22 Jul 2026
2026 NCEPU–UCEER: Green Energy Youth Leadership Summer Camp
DSL co-organised the 2026 Green Energy Youth Leadership Summer Camp in Beijing, bringing together students from UK universities to explore green energy, technological innovation and Chinese culture.
22 Jul 2026
Papers accepted at ICML 2026
Two PhD student-led papers from Dynamic Systems Lab have been accepted as regular papers at ICML 2026.
01 May 2026
