AI PHYSICS SURROGATE MODELING
Faster Design Exploration,
Grounded in Your Simulation Data
Turn your simulation data into fast, physics-grounded AI models with Rescale’s AI Physics. Discover how Rescale brings design space exploration, real-time inference, and multi-physics workflows directly into the tools your engineers already use.
Barriers Blocking Faster Design Exploration
The Speed-Versus-Accuracy Tradeoff
High-fidelity simulation is too time and resource intensive to cover a broad design space, but cutting solver fidelity to move faster risks accuracy gaps that only show up late in development.
The ML Expertise Gap
Building a production surrogate model has traditionally required an ML engineer to pick an architecture, tune hyperparameters, and manage training runs, expertise most simulation teams don’t have on staff.
Static Models, Moving Targets
Surrogate models that aren’t retrained as new simulation data arrives lose accuracy over time, turning every update into a manual project instead of a tool engineers can rely on.
Faster Exploration. Validated Accuracy.
1,000x
Increase in engineering productivity with Agentic Engineering
GM Motorsports
4x
More design candidates evaluated per development cycle using AI surrogates and GPU-accelerated CFD
Boom Supersonic
10x+
Reduction in simulation time and cost, with AI reduced-order models delivering speedups as high as 3,600x on select workloads
Schlumberger
The Fastest Path from Simulation Data to Design Insight
Build Models Without an ML Team
Turn existing simulation data into a validated AI model using guided workflows, default training parameters, and an AI copilot, no custom code or dedicated data science support required.
Explore More Designs, Validate What Matters
Screen thousands of geometry and load cases with surrogate inference in minutes, then send only the top candidates to a high-fidelity solver to confirm the result.
Keep Models Current, Automatically
Connect surrogate training to your existing simulation workflows so models retrain as new results land, keeping predictions accurate without manual rework.
The Best Way To Explore Rescale Is Hands On.
AI Physics Surrogate Modeling Capabilities
DEMOCRATIZED SURROGATE MODEL DEVELOPMENT
Build Production Surrogate
Models Without the
AI/ML Expertise
If your team already runs simulations on Rescale, you already have what you need to build a surrogate model. AI Physics turns your existing results into a structured AI dataset, then guides you through training with default parameters, an AI copilot, and Auto Tune, which automatically explores model configurations to find the most accurate fit. This democratizes surrogate model development for simulation and design teams, giving engineers direct access to physics-based AI models without hiring a data scientist or writing code.
AI-ACCELERATED DESIGN SPACE EXPLORATION & OPTIMIZATION
Screen Thousands of Designs to Expand
Full-Fidelity Simulation
Rescale’s AI Physics inference evaluates thousands of geometry and load cases in minutes using your trained surrogate model, then validates only the top candidates with a high-fidelity solver. Define an objective, set your constraints, and run an optimization sweep directly against the model, so your team spends scarce high-fidelity compute on the handful of designs that matter instead of the entire design space.
EMBEDDED AI PHYSICS FOR DESIGNERS
Real-Time Feedback Within The Tools
Designers Already Use
Real-time surrogate inference integrates directly into CAD and visualization tools like Autodesk Alias and Blender, so designers get instant aerodynamic and structural performance feedback as they model, without leaving their workflow or waiting on a solver run. Fewer handoffs between design and analysis teams means simulation-aware decisions happen earlier in development, when they’re cheapest to act on.
MULTI-PHYSICS & HYBRID SURROGATE WORKFLOWS
Chain Surrogates Across
Physics Domains and Applications
Run AI Physics inference models as nodes inside your existing workflows, coupling predictions across CFD, structural, thermal, and NVH domains, or feeding a surrogate’s output into a traditional solver as an informed starting condition instead of a blank guess. This hybrid approach cuts iteration counts and runtime on complex, multi-physics problems while preserving high-fidelity accuracy where it matters most.
CONTINUOUS RETRAINING PIPELINES
Models That Improve as
Simulation Datasets Grow
Connect AI Physics to your existing simulation workflows so surrogate models retrain automatically as new results land, no manual re-entry, no stale predictions. Every inference comes with a confidence signal that tells you whether to trust the result or validate it with a full simulation, so your team always knows which predictions are ready for production decisions.
GPU CAPACITY FOR AI TRAINING & INFERENCE
Access Scalable Compute to Power Your AI Ambitions
Access the latest GPU architectures at the scale your training and inference workloads need. Rescale connects surrogate model training to high-performance GPU capacity across leading hyperscalers and specialized cloud providers, including NVIDIA DGX Cloud, so training runs on the most advanced hardware available and inference stays fast as your model library grows.
Explore AI Physics Resources
AI Physics Model Deployment for High-Compliance Aerospace Design
AI-augmented simulation dramatically accelerates design evaluations using inference prediction. Rescale’s AI Physics gives aerospace engineers a complete solution to build, deploy, and tune custom AI models using existing simulation data…
Watch Video
GeoTransolver AI Model Architecture Integration for CFD Applications
Power complex and high-accuracy AI surrogate models for CFD and FEA with GeoTransolver model architecture. Now available in Rescale AI Physics as part of the NVIDIA PhysicsNeMo library, GeoTransolver is…
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What Are AI Surrogate Models? A History and How to Get Started.
AI surrogate models have emerged as powerful tools for accelerating engineering design cycles. They provide fast, data-driven approximations of high-fidelity simulations and enable engineers to explore large design spaces more…
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