AI and Predictive Analytics: Comprehensive Analytics

Artificial intelligence (AI) and predictive analytics are reshaping how all businesses operate. This article focuses on engineering applications of AI and predictive analytics. Start with the general concept of artificial intelligence (AI). Learn more about predictive engineering analytics as applied to engineering.

Learn more about artificial intelligence approaches such as machine learning and deep learning. Key differences are highlighted. By the end of this article, you’ll understand how innovative Deep His learning technology leverages historical data to accurately predict the results of time-consuming and expensive experimental tests and 3D simulations (CAE). You will be able to

different analytical strategies

There are different types of analytics strategies such as descriptive analytics, diagnostic analytics, and predictive analytics. What are the main differences?

Descriptive and diagnostic analytics have different focuses. Descriptive analytics means summarizing and interpreting historical data to provide insight into what 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 and manufacturing. For example, predict the performance of new products before testing them in the lab, or identify machine failures and maintenance requirements before an event occurs.

Predictive analytics is the fourth type of analytics that is of interest to engineers. Prescriptive analysis. This includes recommendations for actions to optimize results.

AI and Humans – Competition or Cooperation?

Will AI replace engineers?

No, rather, increased human interaction in product design and predictive maintenance will give us more power to make and influence decisions and use the digital thread smarter. You can AI serves as a powerful tool, empowering engineers and providing actionable insights to enhance the decision-making process.

Artificial intelligence and predictive analytics

Artificial intelligence is a field that involves the development of intelligent machines that can simulate human intelligence and perform tasks that normally require human cognition. Predictive analytics, more specifically, uses data, statistical algorithms, and machine learning techniques to predict future outcomes based on historical and real-time data. This area of ​​analytics leverages past patterns and trends to predict future events, behaviors, or trends with some level of accuracy. Predictive analytics utilizes various machine learning algorithms to build predictive models that can provide insight into future scenarios.

Introduction to AI and predictive analytics

In its broadest sense, artificial intelligence (AI) refers to the development of intelligent machines that simulate human intelligence and can perform tasks that normally require human cognition. Artificial intelligence includes 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.

Predictive analytics, on the other hand, focuses specifically on techniques for accurately predicting future outcomes. Unlike other business intelligence technologies, predictive analytics is forward-looking, using past events (obtained and ordered by data mining) to predict (= predictive) and may reshape it (= normative).

Predictive Analytics Before AI: Traditional 3D Simulation (CAE)

Since the 90s, before AI, engineers have been able to use statistical or physics-based models to provide predictive analytics tools that encompass knowledge about the world.

As an example of a traditional predictive modeling workflow, engineers can predict the aerodynamic performance of a car based on its geometry (CAD=Computer Aided Design) without having to build the car and test it in a wind tunnel. Even if aerodynamics is governed by physical equations such as those of Navier-Stokes, the complex algorithms of engineering predictive analytics require parallel computing to get answers within a reasonable amount of time (days or hours). Requires investment in new hardware.

Predictive Analytics with AI: 3D Simulation (NCS)

Since 2018, Neural Concept has provided a proxy for CAE by leveraging deep learning to learn to build unique predictive models by data mining historical CAE data.

Robust Only Predictive Analytics comprehensively utilizes historical and real-time CAE and CAD data with proprietary data analysis algorithms and machine learning techniques to generate advanced predictive technology that supports human analysts .

At first glance, new predictive analytics workflows based on AI look a lot like CAE because the input is always the design geometry (CAD input), but there are three key differences.

• Predictive analytics results in fractions of hours instead of hours
• All of CAE’s complex algorithms for numerical solutions are replaced by neural networks.
• No special skills are required to use the software tools, as deep learning provides practical predictive models that require only the ability to be fed new input data.

An application engineer involved in product design work does not need to be an AI expert involved in data analysis.

The expert preparation phase can be flexibly managed by internal or external resources with data science expertise, such as the Neural Concepts team.

The majority of our engineers (right) have access to real-time predictive tools without becoming an expert (left)

data analyst job

In the realm of predictive analytics, data analysts play a key role in extracting valuable insights from data.

Data analysts capture historical trends and patterns that serve as the basis for predictive modeling. Once the data is prepared, data scientists use various statistical techniques and algorithms to query the data and uncover trends in the data. Once you’ve identified a trend, you can incorporate it into your predictive analytics machine. In other words, data analysts apply 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 major areas of AI that help predictive analytics.

• ML refers to the development of algorithms that allow 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 how the human brain works, enabling them to process complex, unstructured data with amazing accuracy. increase.

Machine Learning – Details

The scope of machine learning is vast. Machine learning includes a wide range of algorithms such as 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 important considerations in machine learning. High-quality data is essential to effectively train machine learning models. Data scientists identify and collect relevant data from various sources such as databases and web scraping.

Deep Learning – Details

The advantage of deep learning is that it can automatically learn hierarchical representations from raw data. Deep learning is based on deep neural networks consisting of multiple layers of interconnected nodes that process data.

These deep learning models excel at handling complex data types such as images and text, which is why deep learning outperforms traditional machine learning approaches in tasks such as image recognition and natural language processing. .

Deploy predictive analytics solutions powered by AI

Neural Concept has partnered with Airbus to create innovative design solutions to a wide range of aerospace and defense challenges in areas such as fluid dynamics, structural engineering and electromagnetics.

The integration of AI is having a major impact on predictive analytics, such as pressure fields on the exterior of an aircraft. With traditional CCAE methods, this process took about an hour. However, machine learning implementations have reduced this time significantly to 30 ms. This means predictive analytics is over 10,000x faster.

​​

Such acceleration will enable product design teams to create 10,000 more design variations in a given period of time.

Prescriptive Analysis – Use Case

In heat exchanger applications, the NCS AI model demonstrates the ability to accurately predict overall efficiency, temperature, and pressure drop at the outlet of the system. This validated predictive analytics and allowed engineers to iterate on different geometries and topologies while working on new heat exchanger designs.

Additionally, the use of the NCS optimization algorithm library further enhances the configuration of heat exchangers with generative design. This integration of prediction and prescription greatly enhanced the final design through geometry morphing techniques.

NCS (Neural Concept Shape) optimizes the configuration of the heat exchanger.

summary

In conclusion, artificial intelligence (AI) and predictive analytics are transforming business, especially in engineering. AI, including techniques such as machine learning and deep learning, leverages historical data to accurately predict outcomes, reducing the need for expensive experimental testing and simulations.

Predictive analytics focuses on accurately predicting future outcomes based on data, and engineers also benefit from a prescriptive approach that recommends actions for optimization.

The integration of AI into predictive analytics will revolutionize the engineering process, delivering faster results and more efficient design through techniques such as generative design.

New Possibilities for Engineers

The progress shown opens up new possibilities for engineers.

Without having to spend an overnight in Python or a data science class, any engineer can increase their influence on the decision-making process, achieve superior results in all areas of product design, and become a ‘corporate hero’. can be

Note: Thanks to the Neural Concept team for the thought leadership/education article above. Neural Concept supports this content.