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AI for Design & Engineering Workflows
AI is being leveraged across the design cycle in numerous ways. AI and ML-based tools help engineers create better designs faster and take advantage of existing data sets for rapid inference and analysis.
Get this whitepaper to see some of the specific engineering use cases where AI can play an important role.
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AI is integrated into design workflows through tools like generative design, which allows engineers to create multiple optimal design options based on predefined constraints. This approach broadens the design space and enables faster evaluation and selection of designs. Additionally, AI helps automate tasks, analyze large datasets, and improve simulation processes, ultimately leading to quicker and more innovative design solutions.
How do AI and ML improve simulation processes?
AI and ML improve simulation processes by leveraging GPU acceleration to handle larger and more complex models, reducing bottlenecks in the design cycle. For example, tools like Altair's physicsAI can provide rapid predictions without needing a full-scale simulation, achieving speedups of up to 14 times compared to traditional methods. This allows engineers to quickly evaluate designs and make informed decisions.
What are the hardware requirements for AI-based engineering?
To effectively utilize AI-driven design and simulation tools, advanced engineering workstations equipped with the latest NVIDIA RTX GPUs are essential. For instance, the Dell Precision 7960 can be configured with multiple GPUs to handle large datasets and complex models. These workstations enable local AI model training and rapid validation, making them crucial for modern engineering workflows.
AI for Design & Engineering Workflows
published by Computer Hardware, Inc.
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