AI is reshaping how we design and validate complex engineering systems. But for simulation-driven industries, one critical bottleneck remains: data availability. High-quality simulation data is the foundation for trustworthy AI models — yet most organizations struggle with scattered data, proprietary formats, and disconnected workflows.
The result? Costly inefficiencies, limited reuse of existing assets, and missed opportunities to leverage AI effectively. In this white paper, we explore how to take back control of your engineering data and build a sustainable foundation for AI-ready simulation workflows.
What you’ll learn
Inside, we break down:
- Why data availability is the #1 barrier to scaling AI in simulation-based engineering.
- The two main strategies for AI model training — case-specific vs. foundational — and the trade-offs of each.
- Real-world costs of large-scale data generation (including a case study from MIT).
- How to treat your simulation data as a strategic asset, not a byproduct.
- A structured approach to building AI-ready data pipelines without disrupting your existing toolchain.
Why this matters
AI-driven simulation promises faster design iterations, better predictive accuracy, and smarter decision-making. But without high-quality, accessible data, these benefits stay out of reach. Miura Simulation developed Miura Nexus to solve this exact problem. Built on open standards and designed for full interoperability, Nexus centralizes your simulation data, makes it reusable, and exposes AI-ready endpoints — all without forcing you to change your existing tools.