70 citations · 150 across the 31 of their papers we have counts for
16 papers · 1 filter
Physics-informed machine learning for building performance simulation-A review of a nascent field
Zixin Jiang, Xuezheng Wang, Han Li +5
Building performance simulation (BPS) is critical for understanding building dynamics and behavior, analyzing performance of the built environment, optimizing energy efficiency, im…
Safe Physics-Informed Machine Learning for Dynamics and Control
Jan Drgona, Truong X. Nghiem, Thomas Beckers +6
This tutorial paper focuses on safe physics-informed machine learning in the context of dynamics and control, providing a comprehensive overview of how to integrate physical models…
Differentiable Predictive Control for Large-Scale Urban Road Networks
Renukanandan Tumu, Wenceslao Shaw Cortez, Ján Drgoňa +2
Transportation is a major contributor to CO2 emissions, making it essential to optimize traffic networks to reduce energy-related emissions. This paper presents a novel approach to…
A Robust, Efficient Predictive Safety Filter
Wenceslao Shaw Cortez, Jan Drgona, Draguna Vrabie +1
In this paper, we propose a novel predictive safety filter that is robust to bounded perturbations and is implemented in an even-triggered fashion to reduce online computation. The…
Physics-Informed Machine Learning for Modeling and Control of Dynamical Systems
Truong X. Nghiem, Ján Drgoňa, Colin Jones +10
Physics-informed machine learning (PIML) is a set of methods and tools that systematically integrate machine learning (ML) algorithms with physical constraints and abstract mathema…
Differentiable Predictive Control with Safety Guarantees: A Control Barrier Function Approach
Wenceslao Shaw Cortez, Jan Drgona, Aaron Tuor +2
We develop a novel form of differentiable predictive control (DPC) with safety and robustness guarantees based on control barrier functions. DPC is an unsupervised learning-based m…