Digital twin technology has a huge potential for widespread applications in different industrial sectors such as infrastructure, aerospace, and automotive. However, practical adoptions of this technology have been slower, mainly due to a lack of application-specific details. Here we focus on a digital twin framework for linear single-degree-of-freedom structural dynamic systems evolving in two different operational time scales in addition to its intrinsic dynamic time-scale. Our approach strategically separates into two components -- (a) a physics-based nominal model for data processing and response predictions, and (b) a data-driven machine learning model for the time-evolution of the system parameters. The physics-based nominal model is system-specific and selected based on the problem under consideration. On the other hand, the data-driven machine learning model is generic. For tracking the multi-scale evolution of the system parameters, we propose to exploit a mixture of experts as the data-driven model. Within the mixture of experts model, Gaussian Process (GP) is used as the expert model. The primary idea is to let each expert track the evolution of the system parameters at a single time-scale. For learning the hyperparameters of the `mixture of experts using GP', an efficient framework the exploits expectation-maximization and sequential Monte Carlo sampler is used. Performance of the digital twin is illustrated on a multi-timescale dynamical system with stiffness and/or mass variations. The digital twin is found to be robust and yields reasonably accurate results. One exciting feature of the proposed digital twin is its capability to provide reasonable predictions at future time-steps. Aspects related to the data quality and data quantity are also investigated.

AI running in the cloud might be the solution to electric vehicles' battery woes, if Bosch is on the right track. The Stuttgart, Germany-based company this morning announced a new service -- Battery in the Cloud -- designed to supplement vehicles' battery management systems by implementing protections to reduce cell aging. It's able to cut down on wear and tear by as much as 20%, the company claims, through continuous analysis of battery status, optimization of recharging processes, and delivery of energy conservation tips to drivers via in-car displays. The first customer is Beijing-based mobility giant DiDi Chuxing, which as of 2018 had 550 million users and tens of millions of drivers on its platform. Bosch says DiDi will equip a pilot vehicle fleet with its battery services in the city of Xiamen.

This article is part of Forms From the Future, a series in which Rose Eveleth imagines the bureaucratic paperwork of tomorrow and what it says about our impending realities. At many large companies, before you can get a new computer or server or device, you have to fill out a form that tells the company not just why you need the item but who will pay for it and who is responsible for it. But as the sorts of work that humans and machines do collapse down even further, that might change. Let's say you're a manager at a big box store or a fast-food franchise or even a hospital. You might also need to fill out paperwork justifying your request for an actual person to pull boxes from a shelf, check people out at a store, deliver medication, or make a hamburger.

Estimation of the prerequisites for the maintenance, repair, rehabilitation and reconstruction of pavement is one of the requirements for the design and maintenance of the structure of pavement. The pavement design methods are based on providing a proper prediction of the structure of pavement to keep it in permissible condition. The term'remaining service life' (RSL) refers to the time it takes for the pavement to reach an unacceptable status and need to be rehabilitated or reconstructed (Elkins, Thompson, Groerger, Visintine, & Rada, 2013 Elkins, G. E., Thompson, T. M., Groerger, J. L., Visintine, B., & Rada, G. R. (2013). Prediction of the RSL is a basic concept of pavement maintenance planning. Awareness of the future conditions of pavement is a key point in making decisions in the planning of pavement maintenance. On the other hand, we know that pavement optimization methods are urgently needed to predict changes in pavement conditions over a defined period of time.

This post is authored by John Ehrlinger, Data Scientist at Microsoft. Microsoft has recently launched Azure Machine Learning services (AML) to public preview. The updated services include a Workbench application plus command-line tools to assist in developing and managing machine learning solutions through the entire data science life cycle. An Experimentation Service handles the execution of ML experiments and provides project management, Git integration, access control, roaming, and sharing of work. The Model Management Service allows data scientists and dev-ops teams to deploy predictive models into a wide variety of environments.