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Deployable ML Decision-Support Systems for Automotive CAE

30 Chapters
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01
Deployable ML decision-support in automotive CAE
scope, stakeholders, and the gap between simulation and engineering decisions.
Deployabledecision-support
02
CAE data landscape for ML
solver outputs, mesh formats, boundary conditions, load cases, and metadata schemas.
CAElandscape
03
Decision-support framing
mapping CAE objectives (crash, NVH, thermal, durability) to ML problem types and target variables.
Decision-supportCAE
04
Feature engineering from simulation results
field data, scalar KPIs, time series, and geometry-aware descriptors.
simulationKPIs
05
Surrogate modeling for CAE
Gaussian processes, random forests, and neural networks for response prediction.
Surrogatemodeling
06
Dimensionality reduction and latent representations for high-dimensional CAE fields (PCA, autoencoders, POD).
Dimensionality reduction and latent representations for high-dimensional CAE fields (PCA, autoencoders, POD).
Dimensionalityreduction
07
Uncertainty quantification in CAE predictions
aleatoric vs. epistemic, confidence intervals, and calibration.
Uncertaintyquantification
08
Data generation and augmentation strategies
DOE, adaptive sampling, and transfer learning across load cases.
generationaugmentation
09
Model validation for decision-support
cross-validation, holdout sets, and physics-based sanity checks.
validationdecision-support
10
Interpretability and trust
SHAP, partial dependence, and sensitivity analysis for CAE engineers.
InterpretabilitySHAP
11
Deployment constraints in automotive CAE
latency, memory, solver coupling, and HPC integration.
Deploymentconstraints
12
MLOps for CAE
versioning datasets, models, and simulation configurations in regulated environments.
MLOpsCAE
13
Real-time decision-support
streaming sensor data, online updating, and edge deployment in vehicles.
Real-timedecision-support
14
Case study
ML-assisted crashworthiness decision-support for early design phases.
ML-assistedcrashworthiness
15
Case study
NVH prediction and optimization using ML surrogates in body-in-white design.
NVHprediction
16
Case study
thermal management decision-support for battery packs and power electronics.
managementdecision-support
17
Case study
durability and fatigue life prediction with ML from FEA results.
durabilityprediction
18
Multi-fidelity and multi-physics ML
combining coarse and fine simulations for efficient decisions.
Multi-fidelitymulti-physics
19
Optimization and design space exploration
Bayesian optimization, genetic algorithms, and ML-driven trade studies.
Optimizationexploration
20
Human-in-the-loop decision-support
interactive dashboards, what-if analysis, and engineer feedback loops.
Human-in-the-loopdecision-support
21
Safety, certification, and regulatory aspects
ISO 26262, SOTIF, and ML in safety-critical CAE decisions.
certificationregulatory
22
Scalability and cloud/on-prem deployment
containerization, orchestration, and distributed inference.
Scalabilitycloud/on-prem
23
Monitoring and maintenance
drift detection, retraining triggers, and performance degradation in production.
Monitoringmaintenance
24
Integration with CAD/CAE pipelines
APIs, automation, and closed-loop simulation workflows.
IntegrationCAD/CAE
25
Benchmarking and metrics for decision-support
accuracy, robustness, decision quality, and business impact.
Benchmarkingdecision-support
26
Ethical and organizational considerations
bias, accountability, and change management in engineering teams.
organizationalconsiderations
27
Project
end-to-end deployable ML decision-support system for a selected automotive CAE use case.
end-to-enddeployable
28
Advanced topics
physics-informed neural networks, graph neural networks for meshes, and foundation models for CAE.
Advancedphysics-informed
29
Future directions
generative design, digital twins, and autonomous decision-support in automotive engineering.
directionsgenerative
30
Capstone review
synthesizing deployable ML decision-support systems, lessons learned, and best practices.
Capstonesynthesizing