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This page is about the applications of machine learning (ML) in the context of surrogate modelling. For an overview of surrogate models more generally, please see the Wikipedia page on this topic.
Machine learning can approximate simulations by learning how to inputs to outputs. These surrogate models can run considerably faster as they have much lower computational demands. This technique can be used in many different application areas in climate change mitigation and adaptation. For example, building energy simulation (BES) programs are software tools that simulate the complex physics of buildings and are key enabling tools for R&D in the building's domain. However, detailed BES models are notoriously difficult to design, tune and typically have high computational demands. ML, in conjunction with physics, can help to build accurate yet computationally efficient surrogate models for faster simulations.