Description
Learn how to build AI models that use crash simulation data to predict how vehicle structures deform, helping engineers evaluate more designs faster.
This video walks through the NVIDIA PhysicsNeMo workflow, from preparing meshes, design variables and time-series data to training and evaluating an AI surrogate model. Compare one-shot, time-conditional and autoregressive prediction approaches, and see how an optional guardrail can flag unfamiliar inputs for further review.
Get started with the PhysicsNeMo crash training recipe and companion bumper-beam notebooks covering data preparation, model architecture, training, inference and validation.
High-fidelity simulations and physical testing remain essential for validation and final engineering decisions.
Learn how to build AI surrogate models for structural and automotive crash analysis:
https://nvidia.github.io/physicsnemo/blog/2026/03/17/structural-mechanics/
Explore the NVIDIA PhysicsNeMo crash training recipe:
https://github.com/NVIDIA/physicsnemo/tree/main/examples/structural_mechanics/crash
Work through the crash-analysis notebooks:
https://github.com/openhackathons-org/End-to-End-AI-for-Science/tree/main/workspace/python/jupyter_notebook/Crash
Learn Transolver and GeoTransolver from theory through training:
https://github.com/openhackathons-org/End-to-End-AI-for-Science/tree/main/workspace/python/jupyter_notebook/Transolver
Chapters:
00:00 - Introduction & AI Surrogates for Crash Analysis
00:57 - Dataset Creation, Validation & Guardrails
01:36 - Converting Simulation Data to AI Formats
02:15 - Prediction Methods: One-Shot, Time-Conditioned & Autoregressive
02:53 - PhysicsNeMo Crash Recipe Overview
03:50 - Overview of Educational Notebooks
04:10 - Notebook 0: Data Preparation & Inspection
04:45 - Notebook 1: Neural Transolver Architecture & GALE Attention
05:20 - Notebook 2: Training, Inference & Model Evaluation