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Surrogate modeling

Beyond Faster Simulation: Hitting NASA's 2030 CFD Benchmark and the Infrastructure for Physics AI

Beyond Faster Simulation: Hitting NASA's 2030 CFD Benchmark and the Infrastructure for Physics AI

nTop and CoreWeave completed 10,000 LES CFD simulations in about 32 hours, meeting NASA's 2030 CFD Vision goal four years early. The approach combines signed distance field geometry, which cannot break under parametric variation, with a GPU-native Lattice Boltzmann solver that removes the meshing step entirely. The talk also covers the 14 TB dataset generated and the path toward physics AI surrogates trained in implicit latent space.

AslanX Flow in Action: Training & Deploying a CFD Surrogate Model | Deep Dive — Mason Seo

AslanX Flow in Action: Training & Deploying a CFD Surrogate Model | Deep Dive — Mason Seo

Mason Seo, CTO of Narnia Labs, demonstrates AslanX Flow, a platform enabling engineers to train, evaluate, and deploy surrogate models for 3D field prediction (e.g., CFD aerodynamics) without a dedicated machine learning engineer. The deep dive covers building a training workflow from raw engineering data, comparing model architectures like PointNet, Transolver, and Linear Neural Operator, and deploying the winning model as a no-code inference application. A key insight shared is the importance of evaluating models on specific engineering data, as public leaderboard performance doesn't always translate, alongside a preview of AI-driven workflow configuration.

Uncertainty-Guided Data Augmentation for Engineers | Deep Dive - Yongmin Kwon

Uncertainty-Guided Data Augmentation for Engineers | Deep Dive - Yongmin Kwon

This session details a data-efficient method for training engineering surrogate models by using uncertainty quantification (UQ) to guide geometric data augmentation. Instead of random deformations, the approach lets the deep ensemble model identify its own knowledge gaps (epistemic uncertainty), then uses Free-Form Deformation (FFD) to generate new shapes specifically in those uncertain regions. This ensures every expensive simulation run yields maximally informative data, significantly improving model accuracy for a fixed computational budget across domains like structural mechanics and aerodynamics.