There are strengths and weaknesses to both mathematical models and machine learning approaches, for instance mathematical models may be difficult to fully specify or become intractable when representing complex natural or built environments whilst machine learning models can be inscrutable (“black box”) and perform poorly when driven outside of the range of data they have been trained on. At the same time measured data from sensors is becoming increasing available.
We have been working to try and bring the best of both worlds together and we would like to discuss our work and the challenges it presents. Such challenges include model simplification or reduction, model performance in previously unobserved extreme conditions, quantification of uncertainty and techniques to parameterise mathematical models from data.