Promise of Digital Engineering: How it will revolutionize

digital engineeringOne instantiation of the concept of Digital Engineering is the digital twin, a digital model of a physical machine/product, connected to the physical product through data collected using sensors.

By Sarabjot Singh Anand and Anirban Chakraborti, 

What is Digital Engineering?

In the mid-1990’s, an aircraft manufacturer in Northern Ireland had a problem. Their aircrafts were outliving the engineers that built them! Servicing support calls from those flying their planes in developing countries became an issue. They contacted the Northern Ireland Knowledge Engineering Laboratory to help them build an AI based system for them that could institutionalize the knowledge held in the brains of their engineers. The system developed was based on Case-based reasoning and required years of knowledge engineering.

Digital Engineering (DE) aims to solve similar problems. It is defined as “an integrated digital approach that uses authoritative sources of systems” data, and models as a continuum across disciplines to support lifecycle activities from concept through disposal.” One instantiation of the concept of Digital Engineering is the digital twin, a digital model of a physical machine/product, connected to the physical product through data collected using sensors.

Data coupled with Digital Engineering

“Data is the new oil” has now been a mantra in technology circles for at least a decade now, alluding to the fact that data will drive the next industrial revolution just as oil, along with coal, did the first. There is no doubting the fact that we have got very good at generating digital data, whether in entertainment and content consumption or driving innovation through Artificial Intelligence in customer services, agriculture, governance, education or indeed any walk of life. The pandemic has further accelerated the trend towards digital transformation of traditionally offline domains such as education.

Data coupled with hardware in the form of GPUs and TPUs, capable of unleashing more computing power than ever before, and algorithms, some developed decades ago, have created the perfect storm that is set to disrupt everything we know. As wireless sensor networks mature and the cost of sensors drops, the Internet of Everything is set to make most processes digital, making data science a central skill for all, social scientists, fundamental scientists, engineers and business professionals, alike.

Journey of Digital Engineering and the way forward

This highlights another side to Digital Engineering, a journey, that does not start from the engineering drawing board but rather from observing real-world processes. Real-world processes that are not well understood, for example, “complex systems” in biology, finance and business, society or the environment, can be probed using “sensors” to collect data, that can fuel digital exploration and modelling, leading to better insights and optimizations. Take, for example, DeepMind’s AlphaFold. In November 2020, AlphaFold largely solved the problem of predicting a protein’s 3D folding structure from its amino-acid sequence. The 3D structure of a protein is an essential step towards understanding the building blocks of cells, enabling quicker drug discovery and accelerating us towards the bioengineering dream of personalized medicine. What is even more transformational about this development is that this was achieved without expensive laboratory experiments using specialised hardware for X-ray crystallography and, more recently, cryo-EM.

Of course, data is not just revolutionising bioengineering, even traditional professions such as agriculture are being impacted by digital data. Weather, Satellite, multi-spectral images captured by drones or handheld devices and IoT sensors can now create an ecosystem that can help farmers increase yield and profitability through early detection of disease, or pest attacks, or identification of lacking nutrients in the soil for the crop being sown. The promise of precision agriculture that could reengineer age old practices in farming to address the food security crisis facing humanity.

A decade ago, the belief was that everyone needs to learn how to code. The future lies, not with coders but, with those who can engineer solutions by leveraging data.

(The author Sarabjot Singh Anand, Director-Computer Science & Engineering, School of Engineering & Technology, BMU and Dr. Anirban Chakroborti, Dean, School of Engineering & Technology, BMI. Views expressed are personal and do not reflect the official position or policy of the Financial Express Online.)

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