Artificial Intelligence (AI) and Predictive Analytics are reshaping the way all businesses operate. In this article, we will key in on engineering applications of AI and Predictive Analytics. We will start with the general concept of Artificial Intelligence (AI). We’ll go into the details of Predictive Engineering Analytics applied to engineering.
We will give details of Artificial Intelligence approaches such as Machine Learning and Deep Learning. Key differences will be highlighted. By the end of the article, you will understand how innovative Deep Learning technology leverages historical data and accurately forecasts outcomes of lengthy and expensive experimental testing or 3D simulation (CAE).
Different Analytics Strategies
There are many types of analytics strategies: descriptive, diagnostic, and predictive analytics. What are the key differences?
Descriptive and diagnostic analytics have different focuses. Descriptive analytics means summarizing and interpreting historical data to provide insights into what has happened. Diagnostic analytics goes a step further by analyzing data to understand why certain events occurred and identify causal relationships.
Predictive analytics in engineering is rather focused on predicting future outcomes of events in product design or manufacturing, such as respectively predicting the performance of a new product before testing it in the laboratory or identifying machinery malfunctioning and maintenance requirements before the event occurs.
Predictive analytics takes us to a fourth type of analytics of interest to engineers: prescriptive analytics, involving recommending actions to optimize outcomes.
AI and Humans – Competition or Collaboration?
No, rather, it will give them more power to make decisions or influence decisions and use in a smarter way the digital thread with an increase of human interaction in product design and predictive maintenance. AI acts as a powerful tool, augmenting engineers’ capabilities and providing them with actionable insights to enhance their decision-making processes.
Artificial Intelligence and Predictive Analytics
Artificial Intelligence is a field that encompasses the development of intelligent machines capable of simulating human intelligence and performing tasks that typically require human cognition. Predictive Analytics relies more specifically on using data, statistical algorithms, and machine learning techniques to forecast future outcomes based on historical and real-time data. This branch of analytics leverages past patterns and trends to predict future events, behaviors, or trends with a certain level of accuracy. Predictive Analytics utilizes various machine learning algorithms to build predictive models that can provide insights into future scenarios.
Overview of AI and Predictive Analytics
Artificial Intelligence (AI), in its broadest sense, refers to the development of intelligent machines that can simulate human intelligence and perform tasks that typically require human cognition. Artificial Intelligence encompasses a wide range of techniques and algorithms designed to enable machines to learn, reason, and make decisions autonomously.
Artificial Intelligence systems can process and analyze vast amounts of data, identify patterns, and generate insights that drive decision-making and automation.
On the other hand, Predictive Analytics specifically centers on techniques that accurately forecast future outcomes. Unlike other business intelligence technologies, predictive analytics is forward-looking, using past events (retrieved and ordered with data mining) to anticipate the future (=being predictive) or even reshape it (=being prescriptive ).
Predictive Analytics Before AI: Traditional 3D Simulation (CAE)
Before the advent of AI, since the 90s, engineers could provide predictive analytics tools that encompassed their knowledge of the world with statistical or physics-based models.
As an example of traditional predictive modeling workflow, engineers could predict the aerodynamic performance of a car based on its geometry (CAD=Computer Aided Design) without building the car and testing it in a wind tunnel. Even if aerodynamics is governed by physics equations such as Navier-Stokes, engineering predictive analytics’ complex algorithms require investment in hardware with parallel computing in order to provide answers within reasonable times (a few days or hours).
Predictive Analytics with AI: 3D Simulation (NCS)
Since 2018, Neural Concept has been leveraging Deep Learning to provide a surrogate for CAE by learning to build its own predictive models with data mining of past CAE data.
Robust sole predictive analytics relies on the comprehensive utilization of historical and real-time CAE and CAD data, along with proprietary data analytics algorithms and machine learning techniques, to produce advanced forecasting technologies to support human analysts.
The new predictive analytics workflow based on AI looks, at first glance, very similar to CAE since the input is always a design shape (a CAD input), but there are three main differences:
- the predictive analytics result is given in a fraction of a second instead of hours
- all the CAE complex algorithms for numerical solutions are replaced by a neural network
- the usage of the software tool does not require any specific skill because Deep Learning provides a hands-on predictive model that only requires the ability to give new input data
Application engineers involved in product design operations do not need to become AI experts concerned with data analysis.
The preparatory expert phase can be flexibly managed by internal or external resources with data science expertise, such as the Neural Concept team.
The majority of engineers in a company (right) can access to real-time predictive tools without the need to become experts (left)
The Job of a Data Analyst
In the realm of Predictive Analytics, data analysts play a crucial role in unlocking valuable insights from data.
Data analysts capture historical trends and patterns, which serve as the foundation for predictive modeling. Once the data is prepared, data scientists employ a range of statistical techniques and algorithms to query data and uncover data trends; once they identify trends, they can incorporate them in a predictive analytics machine. In other words, a data analyst applies predictive modeling techniques to build models that can predict future outcomes based on historical data.
Machine Learning and Deep Learning
Machine Learning (ML) and Deep Learning (DL) are two key branches of AI that are instrumental in predictive analytics.
- ML refers to the development of algorithms that enable computers to learn from data without explicit programming.
- Deep Learning, on the other hand, is a subset of Machine Learning that focuses on training deep neural networks to mimic the workings of the human brain, allowing them to handle complex and unstructured data with remarkable accuracy.
Machine Learning – Details
The scope of Machine Learning is vast. Machine Learning encompasses a wide range of algorithms, including supervised learning, unsupervised learning, and reinforcement learning. Machine Learning algorithms can be applied to various tasks, such as classification, regression, and clustering.
Data requirements and sources are crucial considerations in Machine Learning. High-quality data is essential for training Machine Learning models effectively. Data scientists identify and gather relevant data from various sources, such as databases or web scraping.
Deep Learning – Details
The advantage of Deep Learning lies in its ability to automatically learn hierarchical representations from raw data. Deep Learning is based on deep neural networks that consist of multiple layers of interconnected nodes that process data.
These Deep Learning models excel in handling complex data types, such as images or text, and this is why Deep Learning has surpassed traditional Machine Learning approaches in tasks like image recognition or natural language processing.
Deployment of a Predictive Analytics Solution with AI
Neural Concept entered into a collaboration with Airbus to generate innovative design solutions for a wide range of aerospace and defense challenges in areas such as fluid dynamics, structural engineering, and electromagnetics.
The integration of AI has had a significant impact on predictive analytics, for instance the pressure field on the external surfaces of aircraft. With traditional CCAE methods, this process used to take around one hour. However, with the implementation of machine learning, the time has been reduced to a remarkable 30 milliseconds. This means an acceleration of predictive analytics of over 10,000 times.
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Such acceleration can empower product design teams with 10,000 more design variations within a given timeframe.
Prescriptive Analytics – Use Case
In a Heat Exchanger application, the NCS AI model demonstrates precise forecasting capabilities for the overall efficiency, temperature, and pressure drop at the outlets of the system. This validated the predictive analytics and enabled engineers to engage with new heat exchanger designs, iterating between various geometries and topologies.
Moreover, the utilization of NCS optimization algorithm library further enhances the configuration of the heat exchanger with generative design. This predictive-prescriptive integration led to significant enhancements in the final design achieved through geometry morphing techniques.
Optimized configuration of heat exchanger with NCS (Neural Concept Shape).
Summary
In conclusion, Artificial Intelligence (AI) and Predictive Analytics are transforming businesses, particularly in the field of engineering. AI, encompassing techniques like Machine Learning and Deep Learning, leverages historical data to accurately forecast outcomes, reducing the need for expensive experimental testing or simulations.
Predictive Analytics focuses on accurately predicting future outcomes based on data, and engineers also benefit from a prescriptive approach, which recommends actions for optimization.
The integration of AI in predictive analytics has revolutionized the engineering process, providing faster results and more efficient designs through techniques like generative design.
New Possibilities for Engineers
The shown advancements open up new possibilities for engineers.
Without needing to embark on overnight Python or Data Science classes, any engineer can improve her/his influence on the decision-making process and achieve superior outcomes in all areas of product design, becoming a “company hero”!