Generative Ai For Aerospace Engineers

1000+ Prompts to design the Aerospace Engineering Workflow

What you'll learn

Understand the fundamentals of Generative AI and its application in aerospace engineering workflows.

Utilize over 1000 prompts tailored for aerospace engineering functions.

Differentiate between LLMs, diffusion models, and multi-modal AI systems used in aerospace.

Apply zero-shot, one-shot, and few-shot prompting techniques in engineering contexts.

Design instructional and analytical prompts tailored for aerospace problem-solving.

Generate conceptual aircraft and spacecraft designs using natural language prompts.

Create structural design variants for wings, fuselages, and assemblies using AI.

Estimate preliminary BOMs and component weights with prompt-based tools.

Develop mission-specific design options aligned with performance constraints.

Prompt AI to generate aerodynamic and CFD-ready inputs from design intent.

Simulate stress, fatigue, and load cases using structured prompt chains.

Detect anomalies in CFD or FEA results using AI summarization capabilities.

Summarize large volumes of test data into actionable performance insights.

Design jet engines and rocket motors using AI-guided architectural prompts.

Model combustion and cooling systems using generative simulations.

Use AI to compare material properties and predict fatigue life.

Diagnose propulsion failures and suggest corrective paths via prompting.

Develop avionics logic narratives and system architectures through AI.

Generate PID control logic and autopilot functions using chain prompts.

Design navigation fault recovery logic and simulate GN&C flows.

Summarize real-time telemetry and flight data into digestible insights.

Plan orbital transfers and optimize spacecraft trajectories using AI.

Configure spacecraft subsystems based on mission type using prompts.

Simulate EDL (Entry, Descent, Landing) scenarios from textual inputs.

Identify deep-space mission risks and auto-generate mitigation logs.

Generate pre-flight checklists and test protocols using structured prompts.

Summarize thermal, wind tunnel, and structural test logs into concise reports.

Automate black box and post-flight event log analysis.

Create FMEA and root cause narratives using AI for audit readiness.

Draft FAA, EASA, and ISO-compliant reports using AI-driven templates.

Generate technical manuals and maintenance SOPs from design data.

Conduct AI-led safety audits and risk log generation.

Write AI-driven quality narratives for manufacturing and inspection.

Apply chaining techniques to simulate end-to-end aerospace workflows.

Requirements

Basic Knowledge of Aerospace Engineering

Description

This course contains the use of artificial intelligence. Generative AI for Aerospace Engineers is designed to equip aerospace engineers with the practical skills and prompt engineering strategies. It equips aerospace engineers with the skills to harness the power of Large Language Models (LLMs), diffusion models, and multi-modal AI to automate and accelerate complex engineering tasks across the entire design, simulation, testing, and operations lifecycle. Beginning with a foundational understanding of how AI integrates into aerospace workflows, the course delves into prompting strategies—zero-shot, one-shot, few-shot—as well as instructional and analytical prompting techniques, enabling engineers to control and customize AI outputs for both creative ideation and technical precision.As the course progresses, learners will explore generative techniques for conceptual aircraft and spacecraft design, including multi-modal generation of structural components like wings and fuselages, and the use of AI to create mission-specific design variants. Prompts for preliminary BOMs (Bills of Materials), weight estimation, and aerodynamic CFD inputs are demonstrated with realistic aerospace scenarios. Critical performance areas such as stress, fatigue, load cases, and anomaly detection in simulation results are handled through structured prompt chains and interpretive AI outputs.Further modules guide learners in AI-assisted propulsion system modeling, from jet engines and rocket motors to combustion cooling systems and material fatigue prediction. Real-world applications in avionics—such as generating auto-pilot logic, fault diagnostics, and real-time flight telemetry summaries—prepare engineers for next-gen autonomous systems. Space-specific modules include orbital trajectory optimization, EDL sequence generation, and spacecraft subsystem configuration.Closing lectures tackle post-flight analysis, black box data summarization, FMEA reports, FAA/EASA/ISO compliance reporting, maintenance SOP generation, and quality inspection narratives. With over 1000 expert prompts, engineers emerge from this course ready to lead AI-integrated innovation across aerospace R&D, testing, flight readiness, and systems engineering.

Overview

Section 1: Foundations of Generative AI in Aerospace Engineering

Lecture 1 Introduction to Generative AI and Its Role in Aerospace

Lecture 2 LLMs, Diffusion Models, and Multi-Modal AI for Engineers

Lecture 3 Course Setup- Downloadable File.

Lecture 4 Zero-shot, One-shot, and Few-shot Prompting Techniques

Lecture 5 Instructional and Analytical Prompts

Section 2: Generative Design and Conceptual Engineering

Lecture 6 AI for Conceptual Aircraft and Spacecraft Design

Lecture 7 Multi-modal Generation of Wing, Fuselage, and Structure Variants

Lecture 8 Prompting for Preliminary BOMs and Weight Estimates

Lecture 9 Generating Mission-Specific Design Options Using AI

Section 3: Simulation and Performance Evaluation

Lecture 10 Generative Prompts for Stress, Fatigue, and Load Cases

Lecture 11 Anomaly Detection in Simulation Results Using AI

Lecture 12 AI Summarization of Performance and Test Metrics

Section 4: Propulsion Systems and Materials Engineering

Lecture 13 Jet Engine and Rocket Motor Design via Prompting

Lecture 14 Cooling Systems and Combustion Modeling Using AI

Lecture 15 AI for Material Selection and Fatigue Prediction

Lecture 16 Prompt-Based Analysis of Propulsion Failures and Efficiency

Section 5: Avionics, Navigation & Control Systems

Lecture 17 Prompting for Avionics System Logic and Functional Narratives

Lecture 18 PID Controller and Auto-Pilot Prompt Generation

Lecture 19 Navigation Fault Scenarios and AI-Powered Recovery Logic

Lecture 20 Real-Time Flight Data and Telemetry Summarization

Section 6: Spacecraft Systems and Mission Planning

Lecture 21 Orbital Mechanics and Trajectory Optimization Prompts

Lecture 22 Spacecraft Subsystem Configuration with AI

Lecture 23 Generating EDL (Entry, Descent, Landing) Sequences

Lecture 24 Prompting for Mission Planning and Deep Space Risk Logs

Section 7: Testing, Validation & Flight Logs

Lecture 25 Pre-Flight Checklist Generation and Test Setup Prompts

Lecture 26 Summarizing Wind Tunnel, Thermal, and Structural Tests

Lecture 27 Post-Flight Log and Black Box Data Summarization

Lecture 28 FMEA and Root Cause Narratives Using Generative AI

Section 8: Documentation, Compliance & Safety

Lecture 29 Generating Compliance Reports for FAA, EASA, and ISO

Lecture 30 AI-Driven Technical Manuals and Maintenance SOPs

Lecture 31 Prompting for Safety Audits and Risk Logs

Lecture 32 Quality Control Narratives for Manufacturing and Inspection

Section 9: 1000+ Prompts - Generative AI for Aerospace Engineers

Lecture 33 Conceptual Aircraft and Spacecraft Design

Lecture 34 Multi-modal Generation of Structural Variants

Lecture 35 Prompting for Initial BOMs and Mass Estimates

Lecture 36 Mission-Specific Design Variant Generation

Lecture 37 Stress and Fatigue Load Case Prompting

Lecture 38 Aerodynamic Flow Simulation Narratives

Lecture 39 Anomaly Detection in CFD or FEM Results

Lecture 40 Summarizing Structural Test Reports

Lecture 41 Jet Engine and Rocket Nozzle Design Prompts

Lecture 42 Combustion Chamber and Cooling System Modeling

Lecture 43 Material Property Comparison and Selection Prompts

Lecture 44 Fatigue Life Prediction from Load Histories

Lecture 45 Propulsion Failure Diagnosis via Prompting

Lecture 46 AI-Based Avionics Logic and Architecture Narratives

Lecture 47 Autopilot and PID Loop Prompt Chains

Lecture 48 Navigation System Fault Tree Analysis Prompts

Lecture 49 Flight Telemetry Summarization and Alert Generation

Lecture 50 Orbital Transfer and Trajectory Optimization Prompts

Lecture 51 Launch Vehicle Staging and Mass Budgeting

Lecture 52 Subsystem Configuration Generation for Spacecraft

Lecture 53 Thermal Balance Calculations Using Generative Prompts

Lecture 54 Entry, Descent, and Landing (EDL) Prompt Workflows

Lecture 55 Power Budget Estimation for Space Missions

Lecture 56 Deep Space Mission Planning Prompts

Lecture 57 Safety Margin Estimation and Worst-Case Scenarios

Lecture 58 FMEA Narratives for Structural and Thermal Systems

Lecture 59 Root Cause Analysis of Post-Test Failures

Lecture 60 Black Box Flight Log Summarization Prompts

Lecture 61 Wind Tunnel Report Generation from Raw Data

Lecture 62 Pre-Flight Checklist Prompting from Design Inputs

Lecture 63 Risk Logs and Hazard Identification Prompts

Lecture 64 Prompting for FAA, EASA, and ISO Compliance Reports

Lecture 65 Technical Manual Drafting from CAD or Specs

Lecture 66 Maintenance SOP Generation from Fault Logs

Lecture 67 Manufacturing QA Narratives for Assembly Inspection

Lecture 68 Prompting for Heat Shield Design and Ablation Modeling

Lecture 69 Sensor Placement Optimization for Structural Tests

Lecture 70 Prompting for Drones and VTOL Configuration Design

Lecture 71 Prompt Chaining for GNC (Guidance, Nav, Control)

Lecture 72 Inspection Summary Generation from NDT Reports

Lecture 73 AI-Powered Weight Distribution and CG Calculations

Lecture 74 Flight Envelope and Stability Margin Estimations

Lecture 75 Cabin Pressurization and Life Support System Design

Lecture 76 Radiation Hardening Prompts for Electronics

Lecture 77 Launch Pad and Ground Support Equipment Narratives

Lecture 78 Design for Additive Manufacturing (DfAM) in Aerospace

Lecture 79 AI Narratives for Certification Test Evidence

Lecture 80 Structural Optimization and Lattice Topology Generation

Lecture 81 Flight Control Surface Tuning Prompts

Lecture 82 Failure Report Summarization and Mitigation Planning

Aerospace Engineers (Aircraft & Spacecraft),Space Mission Designers and System Engineers,R&D and Simulation Specialists,Mechanical and Control Systems Engineers in Aerospace,Design Engineers and CAD Modelers,Flight Test Analysts and QA Inspectors,Researchers and Students in Aerospace Engineering,Manufacturing and Maintenance Professionals in Aerospace