Data Driven Computing and Intelligent Systems: Applications and Advances

Abstract

Data driven computing and intelligent systems are the order of the day. Data-Driven Computing uses the given data to directly produce predictive outcomes. According to data-driven computing, calculations are carried out directly from experimental material data and related constraints and laws of conservation, such as equilibrium and compatibility, thus bypassing the empirical material modelling step of conventional computing completely. Data-driven solvers seek to assign to each material point the state from a pre-specified data set that is nearest to satisfying the laws of conservation. Intelligent systems are technologically advanced machines that perceive and respond to the world around them. They can take many forms, from automated vacuums such as the Roomba to facial recognition programs to Amazon’s personalised shopping suggestions. The field of intelligent systems also focuses on how these intelligent systems interact with end users in the dynamic social and physical environments. Early robots possessed a little autonomy in making decisions. They assumed a predictable world and performed the same actions repeatedly under the similar conditions. Today, a robot is considered to be an autonomous system that can sense the environment and can act in a physical world in order to achieve a set of goals. Data driven computing and intelligent systems both help the mankind in various applications such as good governance, smart cities etc. This study highlights the nature, scope, applications and advances in the data driven computing and intelligent systems.