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Machine Learning Surrogate Models for CAE Design Optimization

30 Chapters

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Course overview

role of surrogate models in CAE design optimization, motivation, and key challenges.

CAE simulation fundamentals for surrogate modeling

governing equations, discretization, and output quantities of interest.

Design of experiments for CAE

sampling strategies, space-filling designs, and adaptive sampling for surrogate training.

Data generation and preprocessing

running CAE simulations, handling mesh variability, and cleaning simulation output.

generationpreprocessing

Feature engineering for CAE surrogates

geometric parameterization, mesh-based descriptors, and field data representation.

Regression surrogate models

polynomial response surfaces, Gaussian processes, and support vector regression for scalar outputs.

Regressionsurrogate

Neural network surrogates for CAE

multilayer perceptrons, convolutional networks for field prediction, and training best practices.

Gaussian process surrogates

kernel selection, hyperparameter tuning, and uncertainty quantification for CAE outputs.

Gaussiansurrogates

Surrogate modeling for field outputs

dimensionality reduction (POD, PCA) and deep learning approaches for full-field prediction.

Surrogatemodeling

Validation and verification of surrogates

error metrics, cross-validation, and holdout testing against CAE simulations.

Validationverification

Uncertainty quantification with surrogates

propagation, confidence intervals, and reliability analysis in design.

Uncertaintyquantification

Surrogate-based optimization

formulation, coupling with optimizers, and handling constraints in CAE design.

Surrogate-basedoptimization

Efficient global optimization

Bayesian optimization, acquisition functions, and infill criteria for CAE problems.

Efficientoptimization

Multi-fidelity surrogate modeling

combining low- and high-fidelity CAE simulations to reduce cost.

Multi-fidelitysurrogate

Active learning for surrogate refinement

adaptive sampling strategies to improve accuracy in critical regions.

surrogaterefinement

Surrogate modeling for transient and dynamic CAE

time-series prediction and reduced-order models.

Surrogatemodeling

Surrogate modeling for nonlinear and multi-physics CAE

challenges and strategies for coupled problems.

Surrogatemodeling

Integration of surrogates into CAE workflows

automation, scripting, and coupling with simulation tools.

Integrationsurrogates

aerodynamic shape optimization using surrogate models.

aerodynamicoptimization

structural design optimization with surrogate-assisted evolutionary algorithms.

structuraloptimization

thermal and fluid systems optimization via surrogates.

optimizationsurrogates

crashworthiness and impact simulation surrogate modeling.

crashworthinesssimulation

Surrogate model interpretability and sensitivity analysis

SHAP, Sobol indices, and engineering insights.

Surrogateinterpretability

Scalability and high-dimensional surrogate modeling

dimensionality reduction and sparse methods.

Scalabilityhigh-dimensional

Surrogate model lifecycle management

versioning, retraining, and deployment in production CAE environments.

Surrogatelifecycle

Benchmarking surrogate models

comparative studies and selection guidelines for CAE applications.

Benchmarkingsurrogate

Hybrid approaches

combining surrogates with physics-informed machine learning and reduced-order models.

approachescombining

Practical implementation

building a surrogate pipeline from CAD to optimization using open-source tools.

implementationbuilding

Industrial deployment challenges

data quality, computational infrastructure, and organizational adoption.

Industrialdeployment

emerging ML techniques, digital twins, and real-time CAE optimization with surrogates.

emergingtechniques