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AI-Based CAE Report Generation and Technical Review

30 Chapters
View Course Samples Online
01
Course overview
scope, key concepts, and the CAE reporting workflow from simulation to sign-off.
CAEreporting
02
CAE data landscape
solver output formats (Nastran, Abaqus, ANSYS, OpenFOAM), result files, and metadata extraction.
CAElandscape
03
Report structure and engineering standards
typical sections, traceability, and compliance requirements for technical review.
structurestandards
04
Introduction to AI for report generation
LLMs, transformers, and their role in automating narrative from simulation data.
generationLLMs
05
Data preprocessing for AI
parsing, cleaning, and normalizing CAE results into structured inputs for generation models.
preprocessingcleaning
06
Template-based vs. AI-driven report generation
trade-offs, hybrid approaches, and when to use each.
Template-basedAI-driven
07
Prompt engineering for CAE
crafting effective prompts to generate accurate technical descriptions from result tables and plots.
CAEcrafting
08
Retrieval-augmented generation (RAG) for CAE knowledge
integrating material properties, load cases, and past reports.
Retrieval-augmentedgeneration
09
Generating textual summaries from contour plots and XY data
vision-language models and chart-to-text techniques.
Generatingsummaries
10
Automated figure and table captioning
extracting key insights from displacement, stress, and frequency results.
Automatedcaptioning
11
Ensuring numerical accuracy
validating AI-generated values against raw solver outputs and unit consistency.
Ensuringnumerical
12
Handling multi-physics and multi-load-case reports
structuring AI prompts for sequential and coupled analyses.
Handlingmulti-physics
13
Domain-specific fine-tuning
adapting LLMs to CAE terminology, boundary conditions, and failure criteria.
Domain-specificfine-tuning
14
Evaluation metrics for AI-generated CAE reports
BLEU, ROUGE, BERTScore, and engineering-specific correctness checks.
EvaluationAI-generated
15
Human-in-the-loop review
designing interfaces for engineers to edit, approve, and audit AI-generated content.
Human-in-the-loopdesigning
16
Technical review automation
AI-assisted checking of assumptions, boundary conditions, and result plausibility.
Technicalautomation
17
Anomaly detection in CAE results
using AI to flag outliers, unconverged solutions, or unrealistic stress concentrations.
detectionCAE
18
Cross-validation with analytical and empirical solutions
AI-driven comparison and discrepancy reporting.
Cross-validationanalytical
19
Version control and traceability
linking AI-generated reports to simulation models, solver versions, and input decks.
traceabilityAI-generated
20
Case study
automated report generation for a structural static analysis using an LLM and template engine.
automatedgeneration
21
Case study
AI-assisted thermal analysis report with parametric study and design of experiments.
AI-assistedanalysis
22
Case study
modal analysis report generation with mode shape description and frequency table summarization.
analysisgeneration
23
Case study
CFD report automation for aerodynamic coefficients and flow visualization narratives.
CFDautomation
24
Case study
fatigue and durability report generation from time-series and rainflow counting results.
durabilitygeneration
25
Case study
crashworthiness report with AI-generated summary of energy absorption and intrusion metrics.
crashworthinessAI-generated
26
Integrating AI report generation into PLM/PDM and simulation data management systems.
Integrating AI report generation into PLM/PDM and simulation data management systems.
Integratinggeneration
27
Security, confidentiality, and IP protection when using cloud-based LLMs for CAE reports.
Security, confidentiality, and IP protection when using cloud-based LLMs for CAE reports.
Securityconfidentiality
28
Regulatory and quality assurance considerations
FDA, FAA, ISO, and internal review boards for AI-generated content.
Regulatoryassurance
29
Future trends
multimodal AI, real-time report generation during simulation, and autonomous CAE agents.
multimodalreal-time
30
Capstone project
end-to-end AI-based CAE report generation and technical review for a real-world simulation case.
Capstoneend-to-end