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CAE Simulation Dataset Engineering for AI Applications

30 Chapters

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CAE simulation dataset engineering for AI

scope, challenges, and end-to-end workflow from problem definition to deployed model.

CAE simulation fundamentals for dataset engineers

governing equations, boundary conditions, solver types, and output fields relevant to AI.

AI/ML requirements for CAE datasets

supervised, unsupervised, and physics-informed learning; data volume, diversity, and labeling needs.

AI/MLrequirements

Simulation dataset design

defining design spaces, parameter sampling (Latin hypercube, Sobol, factorial), and case matrix construction.

Simulationdefining

Geometry parameterization and variation

CAD feature-based, mesh morphing, and free-form deformation for dataset generation.

Geometryparameterization

Mesh generation strategies for AI datasets

structured, unstructured, and overset meshes; mesh quality metrics and automation.

generationstrategies

Solver configuration and automation

scripting (Python, journal files), batch execution, and job scheduling for large-scale runs.

configurationautomation

Data extraction from CAE results

field data (stress, displacement, pressure, temperature), scalar metrics, and metadata capture.

Data storage and management

file formats (HDF5, CGNS, VTK), databases, and metadata schemas for traceability.

Data quality assurance

convergence checks, physical consistency, outlier detection, and automated validation pipelines.

assuranceconvergence

Data cleaning and preprocessing

handling failed runs, missing data, interpolation, and normalization for ML.

cleaningpreprocessing

Feature engineering for CAE data

dimensionality reduction (PCA, autoencoders), invariant descriptors, and graph representations.

CAEdimensionality

Handling unstructured mesh data for AI

graph neural networks, point clouds, and mesh convolution techniques.

Handlingunstructured

Physics-informed data augmentation

exploiting symmetries, scaling laws, and simulation-based synthetic data.

Physics-informedaugmentation

Dataset splitting and sampling strategies

train/validation/test splits, cross-validation, and avoiding data leakage.

splittingsampling

Benchmarking and baseline models

surrogate modeling (Gaussian processes, neural networks) for regression and field prediction.

Benchmarkingbaseline

Uncertainty quantification in CAE datasets

aleatoric and epistemic uncertainty, sensitivity analysis, and robust dataset design.

Uncertaintyquantification

Transfer learning and domain adaptation

leveraging simulation data across geometries, materials, and load cases.

Transferadaptation

Active learning and adaptive sampling

iteratively refining datasets using model uncertainty and error estimates.

adaptivesampling

Scalability and HPC integration

parallel data generation, distributed storage, and cloud-based CAE workflows.

aerodynamic dataset for vehicle drag prediction using CFD and deep learning.

aerodynamicprediction

structural mechanics dataset for stress prediction in automotive components using FEA and CNNs.

structuralmechanics

thermal management dataset for electronics cooling using multiphysics simulation and surrogate models.

managementelectronics

crashworthiness dataset for occupant safety using explicit dynamics and graph neural networks.

crashworthinessoccupant

additive manufacturing dataset for process-structure-property linkage using thermal-mechanical simulation.

additivemanufacturing

Data versioning, reproducibility, and FAIR principles for CAE simulation datasets.

Data versioning, reproducibility, and FAIR principles for CAE simulation datasets.

versioningreproducibility

Ethical and legal considerations

data ownership, export control, and bias in simulation-based AI.

considerationsownership

Deployment and MLOps for CAE AI models

model serving, monitoring, and continuous learning from new simulations.

DeploymentMLOps

Emerging trends

foundation models for physics, differentiable simulation, and generative AI for CAE dataset synthesis.

Emergingfoundation

Capstone project

end-to-end CAE dataset engineering for a real-world AI application, from design to model evaluation.

Capstoneend-to-end