The Intersection of AI and Simulation Technology
How Artificial Intelligence, Machine Learning, and Simulation Work Together
Since the mid-20th century, engineers and scientists have relied on simulation to test, validate, and refine designs. Simulation software has generated vast amounts of synthetic data, running millions of calculations to determine what works and what doesn’t. Today, artificial intelligence (AI) is revolutionizing this process by integrating past learnings with real-time insights, making simulation faster, more efficient, and more accessible than ever before.
Artificial Intelligence (AI), machine learning (ML), and deep learning (DL) are reshaping the digital engineering landscape.
- AI processes massive datasets, identifying patterns and correlations that enhance simulation accuracy and accelerate decision-making.
- ML, a subset of AI, enables computers to automatically learn from test, simulation, and engineering data, optimizing designs and refining predictive models.
- DL takes this further, leveraging multi-layered neural networks to mimic human reasoning, unlocking even deeper insights.

Combining AI and Simulation for Better products, faster development
By combining AI, ML, and simulation, industries gain efficiency and innovation. AI mimics intelligence, ML extracts insights, and simulation models real-world systems. Together, they optimize design, streamline testing, and expand engineering possibilities.
Advantages of AI at product level
- Higher performance: By identifying and correcting errors and by uncovering additional potential, AI enhances product fault tolerance and performance.
- Maximum precision: The analysis of large amounts of data and the recognition of patterns by AI helps to ensure that future products meet the specified requirements with pinpoint accuracy.
- Optimal designs: AI-driven optimization techniques show how product designs can be systematically improved because they are strictly parameter-based.
- More innovation: AI algorithms support the identification and fast verification of new ideas. Information obtained will be reutilized
Advantages of AI at process level
- Decision support: The precise predictions and real-time analyses of AI facilitate decision-making on key development issues.
- Speed: AI shortens iteration loops in the development process and creates additional scope for changes and improvements.
- Cost savings: The automation and optimization of tasks and processes through AI reduces costs.
- Knowledge base: AI facilitates the archiving, evaluation and provision of knowledge from various sources.
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