"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.
twinLab
Understanding Uncertainty Quantification: The Different Types
Tim Dodwell
Feb 5, 2023 · 3 min read
twinLab
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.
Tim Dodwell
Nov 16, 2022 · 1 min read
Knowledge Posts
A New Paradigm for Sewer Network Monitoring
The water sector is currently installing sensors in sewer networks across the globe. In the UK alone, it is expected that water companies will install more than 300,000 sensors by 2030.
Sarah Brooks
Oct 29, 2023 · 4 min read