Introduction
From autonomous vehicles and 3D printing through to smart cities and digital twins, the gap between our physical and digital worlds is growing ever smaller. At the interface of these two worlds is Data-Centric Engineering, a rapidly emerging new branch of science for the 21st century which combines the power and insight available from large-scale data sources with the tools and technology that shape our real-world environment. Bridging the gap between the digital and physical realms requires the development of new machine learning algorithms that are capable of processing large volumes of data from satellites, cell phones, distributed sensors, etc. and making robust and transparent decisions that improve our daily lives and protect our natural environment.
As Data-Centric Engineering is an emerging discipline it needs to define its boundaries, determine the techniques of importance and nurture a diverse community of ECRs to take these ideas forward. This workshop also welcomes contributions in the form of advanced techniques and/or applications on topics relating to Physics-informed machine learning. This is a rapidly emerging subfield, whereby scientific knowledge of systems and phenomena is incorporated into machine learning workflows.
The focus of this workshop is to bring the engineering and machine learning communities together to define Data-Centric Engineering, through keynote talks and contributed sessions, panel discussions, poster presentations and networking.