Being a primary contributor to global energy consumption and energy-related carbon
emissions, the building and building construction sectors are a crucial player in the …

Physics-informed machine learning (PIML) is a set of methods and tools that systematically
integrate machine learning (ML) algorithms with physical constraints and abstract …

Physics-informed neural networks (PINNs) incorporate established physical principles into
the training of deep neural networks, ensuring that they adhere to the underlying physics of …

This paper addresses how physical knowledge can improve machine learning in process
control. A data-driven tracking control framework using physics-informed neural networks …

By considering forecasts and exploiting storage effects, model predictive control can achieve
significant energy and cost savings in the building sector. However, due to the high …

Physics-informed neural networks (PINNs) are one popular approach to incorporate a priori
knowledge about physical systems into the learning framework. PINNs are known to be …

This survey examines the broad suite of methods and models for combining machine
learning with physics knowledge for prediction and forecast, with a focus on partial …

Model Predictive Control (MPC) is a state-of-the-art (SOTA) control technique which requires
solving hard constrained optimization problems iteratively. For uncertain dynamics …

This work concerns the application of physics‐informed neural networks to the modeling and
control of complex robotic systems. Achieving this goal requires extending physics‐informed …

In this article, we propose a physics‐informed learning‐based Koopman modeling approach
and present a Koopman‐based self‐tuning moving horizon estimation design for a class of …