{"id":9566,"date":"2024-12-23T12:36:05","date_gmt":"2024-12-23T12:36:05","guid":{"rendered":"https:\/\/edrmedeso1.wpenginepowered.com\/?post_type=article&p=9566"},"modified":"2026-05-29T10:29:25","modified_gmt":"2026-05-29T09:29:25","slug":"ai-for-engineering-a-beginners-guide-to-getting-started","status":"publish","type":"article","link":"https:\/\/edrmedeso.com\/article\/ai-for-engineering-a-beginners-guide-to-getting-started\/","title":{"rendered":"AI for Engineering: A Beginner\u2019s Guide to Getting Started"},"content":{"rendered":"

AI for Engineering: A Beginner\u2019s Guide to Smarter, Faster Workflows<\/h2>\n

Artificial Intelligence (AI) is revolutionizing engineering workflows by making them smarter, faster and more efficient. This beginner\u2019s guide answers common questions about implementing AI for Engineering, from infrastructure needs to compatibility with existing tools, helping you take your first steps with confidence.<\/p>\n

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What Infrastructure Is Needed to Get Started with AI in Engineering?<\/span><\/h2>\n<\/div>\n

The easiest way to implement AI functionality is through cloud-based solutions. Most providers of AI solutions offer Software-as-a-Service (SaaS) platforms that give you access to a secure domain within their cloud environment. These platforms come with advanced security features and eliminate the need for setting up costly on-premises infrastructure.<\/p>\n

A major reason for the preference for cloud solutions is the high cost of GPU hardware required for AI model training. By leveraging cloud services, companies can bypass the need for upfront investments in such hardware and start using AI without extensive infrastructure setup.<\/p>\n

In summary, with cloud-based AI platforms, you can get started immediately without the need for dedicated infrastructure on your end.<\/p>\n

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How Much Simulation Data Is Required to Train an AI Model?<\/span><\/h2>\n<\/div>\n

The amount of data needed to train a reliable AI model depends on the specific use case. For applications with high variability or complex input parameters, larger datasets are typically required. Conversely, simpler use cases with limited design space can succeed with smaller datasets.<\/p>\n

For example, in the case of Convolutional Neural Networks (CNNs) used in solutions like Neural Concept and Ansys SimAI, you might need a minimum of 50 simulation cases to start developing an effective AI model. However, this is just a rule of thumb, and requirements can vary based on the desired accuracy and complexity of the model.<\/p>\n

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How Long Does It Take to Train an AI Model for Engineering Applications?<\/span><\/h2>\n<\/div>\n

Training an AI model, which involves computational work on GPUs, typically takes anywhere from five-six hours to several days. The duration depends on the complexity of the model and the size of the dataset.<\/p>\n

However, the most time-intensive part of AI model development is data cleaning and preprocessing. Challenges such as inconsistent naming conventions, non-converged models, and outliers in the design space must be addressed before training can begin. Depending on the data complexity, this phase can take days, weeks, and sometimes even months.<\/p>\n

Fortunately, once this work is completed for a model, it doesn\u2019t need to be repeated when adding new data or refining the setup. AI platforms such as Neural Concept provide pipelines \u2013 a series of scripts and functions that capture all preprocessing operations \u2013 enabling efficient workflows for managing multiple models.<\/p>\n

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Will AI Tools Replace Traditional Parametric Tools Like Ansys optiSLang?<\/span><\/h2>\n<\/div>\n

AI tools do not replace traditional tools like Ansys optiSLang \u2013 they complement them. Traditional parametric tools, such as Ansys optiSLang, are very effective for working with parameter spaces, parametric models and creating surrogate models based on parameters. For many use cases, these tools are sufficient and applying an AI framework would most likely be unnecessary.<\/p>\n

However, AI tools excel where parametric tools reach their limits. Scenarios where AI proves advantageous include:<\/p>\n