Dr Yongxiang Lei · Singapore
I develop interpretable, uncertainty-aware learning and control methods for renewable energy, climate forecasting, and industrial systems.
Research Scientist
SIMTech, A*STAR
01 / Research
Connecting data-driven models with the physical systems they help us understand and control.
Research focus
Probabilistic wave prediction accounts for uncertainty in the future excitation of a wave energy converter. Connecting those forecasts to model predictive control brings prediction and energy-conversion decisions into one framework. My broader research interests include wind-turbine pitch control and reinforcement learning.
Methods & interests
02 / Selected work
A selection of published work across climate, renewable energy, industrial modelling, and control.
IEEE Transactions on Automation and Science Engineering
Robust tube-based predictive control for self-balancing during the docking of a reconfigurable unmanned ground vehicle.
ISA Transactions
A Kolmogorov–Arnold network for temperature forecasting, using learnable univariate functions to model temporal relationships.
IEEE Transactions on Instrumentation and Measurement
Interpretable temporal temperature prediction with a Kolmogorov–Arnold neural network.
Explore climate forecasting codeControl Engineering Practice
Bayesian learning for probabilistic process-temperature prediction and uncertainty quantification in aluminum electrolysis.
Applied Soft Computing
Probabilistic diffusion and temporal modelling are connected to model predictive control for wave energy conversion.
IEEE Transactions on Instrumentation and Measurement
A prediction-and-control framework addressing the interactions of industrial mining processes at the plant level.
Control Engineering Practice
Temporal modelling for key-quality prediction in a hierarchical cone thickener.
Journal of Process Control
Self-supervised temporal representation learning for industrial temperature prediction.
Machines
Digital-twin modelling and simulation for the ironmaking process.
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Research monograph
Academic Press / Elsevier
ISBN 9780443367595Theory, Methodology and Applications
Learning-based modelling, prediction, and soft sensing for industrial processes.
Explore the book03 / About
My work brings together machine learning, uncertainty quantification, and the dynamics of physical systems.
I am a Research Scientist at the Singapore Institute of Manufacturing Technology (SIMTech), A*STAR. My research spans interpretable neural networks, probabilistic forecasting, reinforcement learning, model predictive control, and digital twins.
I received my PhD in Mechanical Engineering (Control) from Politecnico di Milano in 2024. My research and teaching at the University of Warwick have focused on learning-based prediction and control for offshore renewable and sustainable energy.
Download curriculum vitaeEngineering · Prediction and control for offshore renewable energy
PhD · Mechanical Engineering (Control)
MSc · Control Engineering
BSc · Automation
Teaching
Graduate teaching at the University of Warwick in nonlinear control, reinforcement learning, and systems modelling.
Academic service
Guest editing on climate forecasting and soft sensing, and prediction and control for offshore energy.
Recognition
04 / Connect
For research collaborations in trustworthy AI, forecasting, predictive control, and digital twins.