"All models are wrong, but some are useful" is a famous quote attributed to the British Statistician George Box. In this series, we explore the field of Uncertainty Quantification (aka UQ), a field which seeks to quantify how wrong a model might be. Uncertainty Quantification is an increasingly important field as engineering and science seek to rely more heavily on simulations and machine learning algorithms to make critical decisions. In this series, we introduce the key concepts, give examples of the principle algorithms and describe the academic challenges at the frontiers of Uncertainty Quantification.
Understanding Uncertainty Quantification: The Different Types
digiLab's digital twin software designs urban solar farms
digiLab leads a novel programme to build digital twin software to design and control next generation urban solar farms.