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.
Understanding Uncertainty Quantification: Propagation of Uncertainty
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