Are you looking to get the most accurate information from your models, and deploy them with confidence? Uncertainty quantification (UQ) is a set of essential computational tools that can help make sure your models are both accurate and reliable. And while UQ can be a complex subject, it doesn't have to be overwhelming! In this blog post, we will break down how Uncertainty Quantification works by discussing the concept of propagation of uncertainty — as well as looking at some practical applications "in the wild!" using two simple uncertainty propagation methods: Monte Carlo Simulation and Polynomial Chaos.
digital twins
Understanding Uncertainty Quantification: Propagation of Uncertainty
digiLab
Feb 6, 2023 · 5 min read
twinLab
Understanding Uncertainty Quantification: The Different Types
"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.
Tim Dodwell
Feb 5, 2023 · 3 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