"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
Breaking the netgain Fusion barrier
How can AI take us the rest of the way to limitless clean energy? This headline could be the most significant in our lifetime. Just this December, 2022, US scientists have announced a decades-awaited breakthrough: a nuclear fusion reactor at the Lawrence Livermore Laboratory in California has created a positive amount of net energy, where more energy was output than was used to maintain the reactor.
Q&A with the founders
digiLab was founded to provide top-tier data science to the engineering industries. A spinout from the University of Exeter, digiLab uses pioneering machine learning to transform the efficiency, resilience and environmental sustainability of its customers. This conversation with the Co-founders of digiLab, Dr Anhad Sandhu and Professor Tim Dodwell, explores the genesis of the company and reveals how digiLab is already optimising approaches to water treatment and nuclear decommissioning.