AI PHYSICS SOLUTION OVERVIEW

Transform Simulation Speed,
Explore More Possibilities

Overcome R&D Limitations with AI-Enhanced Simulation

Product development programs require rapid iteration on boundary conditions and load cases, but full-fidelity simulations demand days or weeks when real-time decision-making needs answers in minutes. Traditional computer aided engineering (CAE) methods require tradeoffs between speed or accuracy: simplified models run faster but sacrifice confidence, while high-fidelity solvers provide precision but bottleneck innovation. Rescale eliminates this tradeoff by transforming existing simulation data into surrogate models that deliver physics-accurate predictions in milliseconds. Rescale’s integrated operating system (OS) for AI physics automates everything from data preparation through production deployment, extracting geometry, meshes, and results without manual reformatting. AI models can deploy as modular, user-friendly services that integrate into design tools or enable remote inference from on-premises environments.

AI-guided design space exploration determines optimal configurations with far less time and computation required.

Business Impact

AI physics accelerates product development while reducing compute costs and expanding design exploration.

  • 1,000x faster evaluation with greater than 99% accuracy achieved by automakers like GM Motorsports in aerodynamics applications
  • 4x more design possibilities explored when combining AI physics with GPU-accelerated CFD, enabling breakthrough innovation
  • 85% cost reduction on evaluation workloads by replacing expensive high-fidelity runs with validated surrogates
  • 10x reduction in wait time for complex simulations, removing bottlenecks from critical path activities

The business outcome of effective AI physics is compressed development timelines. Vehicle development timelines that traditionally span multiple years can be shortened by 50% when AI-first engineering strategies are implemented.

Why Rescale

Rescale provides an integrated AI physics OS platform that spans the complete lifecycle from data structuring through production deployment, purpose-built for engineering workflows.

  • Simulation-Native Architecture: Deep integration with CAE and test pipelines using physics-informed data models that understand solver formats and mesh structures, eliminating months of custom development.
  • Production-Ready Evaluation: Visual error analysis, edge case handling, and validation frameworks satisfy safety-critical and regulated environments, moving beyond experimentation into production capability.
  • Flexible Deployment Options: Publish models as modular servers, embed them in design tools, or enable remote inference from on-premises environments while maintaining governance and audit trails.
  • Open Architecture: Support for multiple AI frameworks and evolving model architectures maintains technology flexibility while connecting to Rescale’s broad solver and infrastructure ecosystem.
Stream design performance predictions directly into the CAD viewport for real-time design feedback.

Use Cases

  • Surrogate Model Development and Deployment: Build trained models for CFD, FEA, thermal, NVH, and materials applications, then publish them for inference in engineering workflows, enabling design teams to evaluate performance in real time while maintaining accuracy.
  • AI Digital Twins: Combine virtual simulation datasets with sensor and operational data for predictive maintenance and in-service optimization, reducing downtime and extending product life.
  • AI-Accelerated Design Space Exploration: Evaluate thousands of geometry or load case variations in minutes, then selectively validate top candidates with high-fidelity solvers, expanding innovation possibilities within the same timeline.
  • Embedded AI Physics for Designers: Provide real-time performance feedback directly in CAD tools, enabling designers to make informed decisions without waiting for simulation results.
  • Multi-Physics and Hybrid Workflows: Orchestrate multiple surrogates with shared inputs, then feed predictions into traditional solvers as improved initial conditions, accelerating convergence and reducing compute time.
Real-time digital twin fuses CFD and live telemetry to reveal airflow patterns for predictive design.

Who Benefits

Learn More About AI Physics on Rescale

Discover how leading engineering teams are deploying production-ready AI methods with Rescale AI Physics to accelerate development timelines and expand design exploration. Learn more.

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