Structural Analysis
Develop structural analysis pipelines with AI, GPU-accelerated solvers, and interactive structural digital twins.
Workloads
Simulation / Modeling / Design
Industries
Automotive / Transportation
Manufacturing
Energy
Aerospace
Business Goal
Innovation
Risk Mitigation
Overview
Why Structural Analysis?
Developers are building structural analysis solutions to help engineering teams predict how products, components, and systems respond to force, impact, heat, vibration, and coupled physics before physical testing. These solutions span finite element analysis, crash analysis, thermal analysis, multiphysics simulation, and noise, vibration, and harshness (NVH).
As product complexity accelerates, relying on traditional computer-aided engineering workflows can limit design exploration. High-fidelity solver runs are often too slow to support rapid iteration, forcing engineers to rely on fewer design variations and higher prototyping costs.
With structural simulation powered by NVIDIA accelerated computing, developers can:
- Run more simulation iterations within the same development window.
- Explore structural design options in near real time with AI surrogate models.
- Automate repetitive simulation setup, analysis, and validation tasks so engineers can focus on higher-value design decisions.
- Accelerate solver performance across workstations, data centers, and cloud infrastructure.
- Visualize time-varying structural results in interactive 3D environments.
- Preserve trusted solver workflows while using AI to guide early design exploration.
Quick Links
Technical Implementation
Build AI-Accelerated Structural Analysis Workflows
Developers can integrate NVIDIA’s full-stack accelerated computing and AI libraries to deliver high-performance, scalable structural analysis capabilities. This pipeline enables the rapid development of custom solvers, AI surrogates, and interactive digital twins that integrate directly into existing CAE ecosystems.
1. Accelerate compute-intensive solvers.
Leverage the NVIDIA CUDA-X library suite to optimize the heavy lifting in sparse linear algebra and iterative solver operations. By integrating cuDSS, cuSOLVER, and AmgX, developers can achieve significant speedups for sparse matrix operations, ensuring that the underlying computational engine supports high-fidelity analysis performance across on-premise data centers or cloud infrastructure.
2. Optimize data preparation pipelines.
Implement efficient data handling for deep learning by utilizing NVIDIA PhysicsNeMo Curator. Build automated ingestion pipelines that transform disparate simulation outputs—such as LS-DYNA, OpenRadioss, VTP, and Zarr formats—into high-quality, normalized datasets ready for training.
3. Train structural mechanics surrogate models.
Use NVIDIA PhysicsNeMo to train AI models that learn from high-fidelity simulation data. For crash and transient structural dynamics, developers can use architectures such as MeshGraphNet, Transolver, GeoTransolver, and related graph or operator-based models to predict deformation and other structural response fields.
4. Deploy scalable inference and validation.
Integrate high-performance inference endpoints that allow end-customers to run predictions for new design variations. Architect solutions that utilize AI surrogates for rapid early-stage exploration while maintaining hooks for trusted high-fidelity solver validation, ensuring model accuracy and engineering compliance.
5. Deliver interactive visualization.
Incorporate Kit-CAE and NVIDIA Omniverse APIs into your solution to provide seamless, GPU-accelerated visualization. Enable end-users to interactively visualize complex temporal structural data, including deforming meshes, plastic strain, and contact pressure, directly within your custom application environment.
Quick Links
Partner Ecosystem
Ecosystem
NVIDIA works with leading engineering software providers to bring accelerated computing and AI into CAE, simulation, and design automation workflows. Featured ecosystem partners include Cadence, Dassault Systèmes, Siemens, Synopsys, Synera, and nTop. Together, these partners help developers and engineering teams accelerate solver performance, automate simulation workflows, explore designs with AI, and build more interactive digital twin experiences.
Quick Links
FAQs
Structural analysis is the broader engineering discipline of evaluating how products, components, and systems respond to loads, impact, vibration, heat, and other physical forces. Finite element analysis (FEA) is one of the most common numerical methods used to perform structural analysis by breaking complex geometry into smaller elements and solving how each part behaves under defined conditions.
In practice, structural analysis includes FEA, crash analysis, fatigue analysis, thermal stress, NVH, multiphysics simulation, physical testing, and AI surrogate modeling.
AI physics learns from high-fidelity simulation data from the ecosystem solver and generates faster predictions for new designs or operating conditions. Instead of waiting for every early-stage design option to complete a full solver run, engineers can use AI surrogate models to explore trends, compare alternatives, and narrow the design space more quickly.
Trusted solvers remain essential for final validation, but AI physics can help accelerate early design exploration, sensitivity studies, and repetitive analysis tasks.
No. AI physics does not replace traditional FEA or crash solvers—it complements them so more simulations, analysis, and optimizations can be done to bring better products to market. AI surrogate models are best used to accelerate early design exploration, sensitivity studies, and what-if analysis by producing faster predictions based on trusted simulation data.
High-fidelity FEA and crash solvers remain critical for detailed engineering validation, certification-sensitive decisions, and final design signoff.
Quick Links
Get Started
Build AI Surrogate Models for Structural Mechanics
Learn how to prepare simulation data, train AI physics models, run inference, and evaluate surrogate model predictions for crash and transient structural mechanics workflows.