AI and Predictive Analytics are revolutionizing businesses. This article will focus on how AI and Predictive Analytics are used in engineering applications.
Artificial Intelligence, or AI, refers to the development of intelligent machines that can perform tasks that typically require human intelligence. It encompasses various techniques and algorithms that enable machines to learn, reason, and make decisions on their own. Predictive Analytics, on the other hand, uses data, statistical algorithms, and machine learning techniques to forecast future outcomes based on historical and real-time data.
There are different types of analytics strategies: descriptive, diagnostic, predictive, and prescriptive analytics. Descriptive analytics involves summarizing and interpreting historical data, while diagnostic analytics goes a step further by analyzing data to understand why certain events occurred. Predictive analytics in engineering is focused on predicting future outcomes in product design or manufacturing, such as predicting the performance of a new product or identifying machinery malfunctioning. Prescriptive analytics recommends actions to optimize outcomes.
AI doesn’t replace engineers. Instead, it empowers them by providing actionable insights and enhancing their decision-making processes. AI acts as a tool to augment engineers’ capabilities and increase human interaction in product design and predictive maintenance.
Predictive Analytics utilizes machine learning algorithms to build predictive models that can provide insights into future scenarios. By leveraging historical and real-time data, predictive analytics can accurately forecast outcomes.
In the past, engineers used traditional 3D simulation (CAE) to predict outcomes. However, with the advent of AI, companies like Neural Concept have been able to leverage Deep Learning to provide faster and more accurate predictive analytics. Deep Learning uses neural networks to process complex and unstructured data.
Data analysts play a crucial role in predictive analytics by capturing historical trends and patterns to build models that can predict future outcomes. Machine Learning and Deep Learning are two branches of AI that are essential in predictive analytics. Machine Learning involves algorithms that enable computers to learn from data, while Deep Learning focuses on training deep neural networks to handle complex data types.
One example of the application of AI and Predictive Analytics in engineering is the collaboration between Neural Concept and Airbus. By implementing machine learning, the time for predictive analytics in areas like fluid dynamics and structural engineering has been significantly reduced.
In conclusion, AI and Predictive Analytics are transforming the field of engineering. By leveraging historical and real-time data, engineers can accurately predict future outcomes and make informed decisions.