nTop Physics AI
Transform weeks of iterative analysis work into hours of intelligent exploration with physics-informed, AI-accelerated simulation and inverse design.
From Tedious Analysis to Predictive Design
nTop Physics AI transforms the traditional serial design loop into a parallel process that frontloads learning and accelerates decision-making. By training surrogate models on thousands of simulations, engineers can explore entire design spaces in real-time, predicting aerodynamic performance, structural behavior, and thermal characteristics instantly. nTop gives you the modeling power, rapid simulation, and data structure to accelerate your AI initiatives without compromising traceability or engineering control.
nTop Physics AI Workflow
nTop Physics AI at Lockheed Martin
nTop partnered with Lockheed Martin to accelerate heat exchanger design using inverse design workflows, where performance parameters drive an optimizer that calls neural networks to find optimal design configurations. This moved the process beyond trial and error into true predictive design.
- 400+ design points computed in 6-8 hours
- Zero computational failures throughout the entire process
- Weeks to hours traditional vs. ML-accelerated timeline
- Millisecond evaluation times for full flow predictions
- Minutes to optimization identifying optimal performance parameters
Parametric Control Over Training Data
Unlike generic geometry models that limit design exploration to pre-determined spaces, nTop's implicit modeling enables engineers to define application-specific parameters that matter for their designs. This means training datasets capture the actual design variables engineers control rather than abstract latent space parameters that provide no engineering insight.
Traceable Design Intelligence
Physics AI models trained on nTop's parametric datasets maintain clear relationships between design inputs and performance outcomes. Engineers understand exactly how optimal configurations were achieved and can modify specific parameters with confidence.
Real-Time Parametric Models That Won’t Break
nTop's implicit modeling technology eliminates the geometry failures that plague traditional CAD-based AI training. This reliability is essential for generating the hundreds of training variants needed for robust Physics AI models.
Simulation-Ready Geometry
Traditional CFD involves complex meshing and lengthy run times, making it impractical for generating large training datasets. nTop's integrated CFD and partnerships with 3rd party CAE tools addresses this by working directly from the implicit model without extensive model prep and meshing.
Why Engineering Leaders Deploy nTop for Physics AI
De-Risk Programs Earlier
If your design has only a few reference points, the uncertainty band is huge. But if the design is generated after thousands of iterations, the risk goes down considerably. Physics AI enables extensive design space exploration before critical downselection decisions, reducing costly late-stage surprises and program overruns.
Accelerate Proposal Development
Explore thousands of design iterations in hours instead of weeks, enabling more refined offerings that increase probability of win. When RFPs demand aggressive timelines, Physics AI delivers the speed advantage needed to submit comprehensive, optimized solutions while competitors are still iterating their first concepts.
Build Valuable IP
Organizations deploying Physics AI workflows create proprietary datasets and surrogate models that become competitive assets. As these models learn from more projects, they provide increasingly sophisticated design intelligence that competitors using traditional methods cannot match.
Scale Engineering Impact
Top engineers can encode their expertise into reusable workflows that multiply their impact across programs. Physics AI enables lights-off design exploration, freeing senior talent for higher-value strategic work while junior engineers leverage AI-powered design intelligence.