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Retrieval-Augmented AI Assistants for CAE Engineering

30 Chapters
View Course Samples Online
01
Course overview
scope, concepts, and technical context of retrieval-augmented AI assistants for CAE engineering.
technicalretrieval-augmented
02
CAE engineering knowledge landscape
simulation domains, solver documentation, material databases, and best practices.
CAElandscape
03
Retrieval-augmented generation architecture
components, data flow, and integration patterns for CAE workflows.
Retrieval-augmentedgeneration
04
Document ingestion and preprocessing for CAE
parsing solver manuals, tutorials, and legacy reports.
Documentingestion
05
Chunking strategies for technical CAE content
preserving equations, boundary conditions, and solver settings.
Chunkingstrategies
06
Embedding models for engineering text
domain adaptation, fine-tuning, and evaluation on CAE terminology.
Embeddingadaptation
07
Vector databases and indexing
selecting and configuring stores for CAE document collections.
databasesindexing
08
Retrieval strategies
sparse, dense, and hybrid search for CAE queries.
Retrievalstrategies
09
Query understanding for CAE
handling solver-specific syntax, units, and multi-part engineering questions.
understandingCAE
10
Context assembly and prompt engineering for CAE assistants
injecting retrieved evidence into LLM prompts.
assemblyCAE
11
Grounding and citation in CAE responses
linking answers to source documents and solver references.
Groundingcitation
12
Handling numerical and tabular data in retrieval
material properties, convergence tables, and result summaries.
Handlingnumerical
13
Integrating CAE simulation results
retrieving and reasoning over output files and post-processing data.
IntegratingCAE
14
Assistant for pre-processing
geometry cleanup, meshing guidelines, and boundary condition setup.
Assistantpre-processing
15
Assistant for solver setup
selecting solvers, setting convergence criteria, and troubleshooting errors.
Assistantselecting
16
Assistant for post-processing
interpreting contour plots, extracting quantities of interest, and validation.
Assistantpost-processing
17
Multi-turn dialogue for CAE troubleshooting
maintaining context across iterative debugging sessions.
Multi-turndialogue
18
Domain-specific fine-tuning of LLMs for CAE
instruction tuning and parameter-efficient methods.
Domain-specificfine-tuning
19
Evaluation metrics for CAE assistants
correctness, grounding, numerical accuracy, and engineer trust.
EvaluationCAE
20
Building a CAE knowledge base
curating internal reports, standards, and simulation templates.
BuildingCAE
21
Security and access control
handling proprietary geometry, material data, and export-controlled information.
Securityhandling
22
Deployment architectures
on-premise, cloud, and hybrid setups for CAE assistant integration.
Deploymentarchitectures
23
Latency and scalability
optimizing retrieval and generation for interactive CAE workflows.
scalabilityoptimizing
24
User interface design for CAE assistants
integration with CAD/CAE GUIs and command-line tools.
interfaceCAE
25
Case study
assistant for structural FEA setup and result interpretation.
assistantstructural
26
Case study
assistant for CFD simulation guidance and convergence troubleshooting.
assistantCFD
27
Case study
assistant for thermal analysis and material selection.
assistantanalysis
28
Human-in-the-loop workflows
verification, feedback collection, and continuous improvement.
Human-in-the-loopworkflows
29
Regulatory and quality considerations
traceability, reproducibility, and validation of AI-assisted CAE.
Regulatoryconsiderations
30
Future directions
multimodal retrieval, simulation-aware agents, and autonomous CAE workflows.
directionsmultimodal