Databricks & Rolls-Royce: Generative AI for Engineering Design
Client
Rolls-Royce
Industry
Aerospace & Engineering
AI Tech Solution
Generative AI for Design Optimization
Solution Provider
Databricks
Challenge
Rolls-Royce, a leader in aerospace and engineering, sought to enhance its design space exploration capabilities by leveraging AI-driven simulation models. The company traditionally relied on parametric models and computational simulations, but these approaches had limitations in handling complex, multi-objective design constraints.
The engineering team faced several challenges:
Inefficiencies in AI model training due to limitations in data volume, structure, and availability.
High computational costs for running simulations across numerous design parameters.
Lack of reusable legacy simulation data, making it difficult to optimize new designs without starting from scratch.
To overcome these challenges, Rolls-Royce needed a scalable AI-driven data intelligence platform that could integrate numerical, text, and image data to power next-generation design models.
Solution
The company developed an AI-powered data intelligence framework using Databricks Mosaic AI tools, which included:
Data Modeling: Optimizing data tables with identity columns and structured formats to improve AI-driven simulations.
ML Model Training: Using 2D representations of 3D simulation results to enhance generative models.
Implementation: Embedding knowledge of unsuccessful solutions into training datasets to guide the neural network towards more effective design choices.
Optimization: Using multi-objective constraint handling to balance multiple design factors, such as reducing weight while increasing efficiency.
With Databricks' scalable infrastructure, Rolls-Royce was able to train and deploy AI-driven models faster and more efficiently, transforming the way the company approached early-stage design exploration.
Results
By leveraging Databricks Mosaic AI, Rolls-Royce significantly improved its AI-driven design processes and model accuracy.
Reduced total cost of ownership (TCO): Databricks provided a unified lakehouse architecture, cutting AI training costs while improving efficiency.
Accelerated time-to-model: The Mosaic AI framework enabled faster model training and deployment, reducing AI development complexity.
Improved model accuracy: The integration of MLflow and AutoML facilitated faster tuning, hyperparameter studies, and optimization of AI models.
Enhanced governance and security: Using Databricks Unity Catalog, Rolls-Royce established a strong AI governance framework for managing and protecting sensitive data.
With Databricks Mosaic AI, Rolls-Royce successfully transitioned from traditional parametric modeling to AI-powered generative design, enabling faster, smarter, and more efficient engineering innovations.
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