{"id":25653,"date":"2021-04-06T07:00:00","date_gmt":"2021-04-06T11:00:00","guid":{"rendered":"https:\/\/blogs.sw.siemens.com\/simcenter\/?p=25653"},"modified":"2026-03-26T06:45:07","modified_gmt":"2026-03-26T10:45:07","slug":"4-myths-about-ai-in-cfd","status":"publish","type":"post","link":"https:\/\/blogs.sw.siemens.com\/simcenter\/4-myths-about-ai-in-cfd\/","title":{"rendered":"4 Myths about AI in CFD"},"content":{"rendered":"\n

This blog was co-written with Krishna Veeraraghavan, Project Manager for Simcenter Engineering Services.<\/strong><\/em><\/p>\n\n\n\n

As Artificial Intelligence (AI) has become more ubiquitous in our everyday lives, so too has confusion about what it is and what it means for engineers. The definition of AI and the role it will play in future product development is often dependent on who you ask.  <\/p>\n\n\n\n

\u201cThe development of full artificial intelligence could spell the end of the human race.\u201d<\/em> 
\u2014 Stephen Hawking (2014) <\/em> <\/p>\n\n\n\n

\u201cArtificial intelligence would be the ultimate version of Google. The ultimate search engine that would understand everything on the web.\u201d<\/em> 
\u2014Larry Page (2000)<\/em> <\/p>\n\n\n\n

\u201cNobody phrases it this way, but I think that artificial intelligence is almost a humanities discipline. It’s really an attempt to understand human intelligence and human cognition.\u201d<\/em> 
\u2014Sebastian Thrun (2013)<\/em> <\/p>\n\n\n\n

So, what is AI exactly? For the purposes of this blog, we will focus only on the role of AI in CFD. We will also clear up some common myths around AI in CFD. But first, let\u2019s establish some basic definitions. <\/p>\n\n\n\n

(Before you begin reading, please feel free to share your thoughts on the state of AI in CFD by completing a brief survey<\/a>).<\/em> <\/strong><\/p>\n\n\n\n

What is AI? <\/h2>\n\n\n\n

AI is a multidisciplinary topic that enables machines, devices, and computers to think and make decisions in a way that would seem intelligent. AI helps machines and programs make smarter decisions by learning and improving in an iterative process based on the information they collect.  <\/p>\n\n\n\n

In the future, AI in engineering may focus more on collective intelligence. In this scenario, engineers and computers may work together in feedback loops to identify problems and develop solutions that can make the design process and systems work more efficiently. This would enhance the capabilities and contributions of the engineers. <\/p>\n\n\n\n

\"What<\/figure>\n\n\n\n

Collective intelligence<\/h3>\n\n\n\n

Collective intelligence refers to ways that data and technologies bring people, machines, and computers together to achieve outcomes that were previously beyond our expectations. Psychologists define collective intelligence as groups whose general ability is to perform well not on a single task, but on a wide range of different tasks. This is possible to achieve by combining human intelligence and machine learning.  <\/p>\n\n\n\n

\"What<\/figure>\n\n\n\n

Machine learning  <\/h3>\n\n\n\n

Machine Learning (ML), a subset of AI, is a self-adaptive algorithm that uses statistical algorithms to build and train systems and models. These systems and models will have the ability to learn and improve without being explicitly programmed to do so and can make predictions or identify hidden patterns in the data. Two types of machine learning widely used are supervised and unsupervised learning. All supervised learning will fall under either regression or classification problem. Input data for both cases can be numerical and categorical. Below are some general guidelines on which techniques are useful: <\/p>\n\n\n\n