AI Physics

AI Physics is an AI-powered digital engineering platform designed to transform engineering workflows by integrating compute, data, and AI. It accelerates modeling and simulation processes at an enterprise scale, offering capabilities from synthetic data generation to rapid solver predictions. This comprehensive stack allows for the deployment of AI-enhanced methods that significantly increase speed and accuracy, enabling the exploration of limitless innovation possibilities.

AI Model Training and Deployment

The platform provides a complete AI stack for custom model development, allowing CAE engineers to build production-ready AI models without requiring deep AI expertise. It offers an end-to-end framework that manages data generation, structuring, dataset creation, model training, evaluation, deployment, and inference services. Automated workflows ensure traceability and version control at an enterprise level, transforming simulation data into powerful AI models.

AI Inference for Simulation

AI Physics accelerates results with real-time simulation predictions, enabling rapid exploration of large design spaces. It delivers results in seconds, matching the accuracy of traditional simulation methods while facilitating rapid iteration and optimization. The system integrates seamlessly with existing engineering workflows, providing instant feedback on design changes and parameter variations.

AI Model and App Library

Users can access a comprehensive library of custom and pre-built third-party AI model architectures optimized for engineering applications. This library allows for the deployment of validated models for common use cases or customization of existing models to meet specific requirements, thereby accelerating project timelines with proven AI solutions.

AI Engineering Workflows

The platform unlocks engineering data intelligence by analyzing vast amounts of simulation data to uncover hidden insights. Features like natural language search, intelligent job result summaries, and pattern recognition enhance decision-making. Smart tagging organizes simulations by context, and AI-driven job summaries convert technical results into easy-to-read visuals and executive-ready reports.

AI Physics has been successfully used by organizations like GM Motorsports to optimize aerodynamics in high-speed racing, significantly reducing R&D cycle times and lap times. The platform offers substantial improvements in design evaluation speed, accuracy, and computing resource efficiency, making it a valuable asset for engineering teams looking to enhance productivity and innovation.

With AI Physics, CAE engineers can build production models that improve productivity by over tenfold without needing AI expertise. The platform covers all aspects of AI model development, from data generation to deployment, enabling organizations to achieve faster time-to-market, more efficient supply chains, and reduced R&D costs.