Physics-Informed Neural Net for Aeroelasticity
Train a neural network that respects the laws of physics
Build a physics-informed neural network (PINN) using DeepXDE to solve aeroelastic flutter equations. Combine deep learning with structural dynamics to predict wing flutter boundaries.
Start Project → AI/MLPredict Airfoil Lift with PyTorch
Replace a wind tunnel with a neural network that predicts lift in milliseconds.
Train a fully-connected neural network in PyTorch to predict the lift coefficient of an airfoil from its geometric shape parameters. You will use NASA's open airfoil database as training data and learn how ML can accelerate aerodynamic design.
Start Project → AI/MLTrain a Drone to Hover with PyTorch RL
Implement PPO from scratch and watch a quadrotor learn to balance itself.
Implement the Proximal Policy Optimisation (PPO) algorithm from scratch in PyTorch and train a quadrotor to hover and track waypoints in a lightweight physics simulation. By building PPO yourself — actor-critic networks, GAE advantage estimation, clipped surrogate loss — you gain deep understanding of why modern RL algorithms work.
Start Project → AI/MLGraph Neural Network for Microstructure-Property Prediction
Turn microscope images into graphs and predict how strong the metal is
Represent alloy or composite microstructure images as graphs — with grains as nodes and boundaries as edges — and train a graph neural network to predict mechanical properties like yield strength and fatigue life. A research-grade application of geometric deep learning to materials science.
Start Project → AI/MLML Model for Combustion Instability Detection
Train a neural network to hear when a combustor is about to go unstable
Train a time-series classifier — LSTM or 1D-CNN — on pressure oscillation data to detect thermoacoustic instability in combustion chambers. Addresses a critical safety problem in rocket engines and gas turbines with deep learning on sequential sensor data.
Start Project → AI/MLPredict Aircraft Noise Levels with Neural Networks
Use NASA wind tunnel data to predict how loud an airfoil is
Use the NASA Airfoil Self-Noise dataset from the UCI Machine Learning Repository to train a neural network that predicts sound pressure level from airfoil geometry, wind speed, and angle of attack. A clean regression problem connecting aeroacoustics with deep learning.
Start Project → AI/MLEngine Sound Anomaly Detection with Autoencoders
Teach a neural network what normal sounds like — then catch everything else
Train a convolutional autoencoder on mel-spectrograms of normal engine audio to detect anomalous sounds using reconstruction error as the anomaly score. Addresses the practical challenge of detecting unknown fault types without labeled failure data.
Start Project → AI/MLML-Optimized Flight Route Planning Around Weather
Route aircraft around storms and turbulence with graph search and ML
Build a system that ingests weather forecast grids and uses graph search with an ML-learned cost model to find fuel-optimal routes that avoid turbulence and convective weather. Compare against great-circle routes to quantify fuel and safety improvements.
Start Project → AI/MLDetect Surface Defects in Aerospace Parts with CNN
Train a deep learning model on real industrial defect data
Train a convolutional neural network to classify images of metal surfaces as defective or non-defective using the NEU Surface Defect Database. Build a production-quality defect detection pipeline applicable to aerospace manufacturing.
Start Project → AI/MLProcess Parameter Optimization for Additive Manufacturing
Use Bayesian optimization to tune 3D printing for aerospace-grade quality
Build a Bayesian optimization pipeline that tunes additive manufacturing parameters (laser power, scan speed, layer thickness) to minimize porosity and maximize tensile strength. Apply surrogate modeling and intelligent search to a real aerospace manufacturing challenge.
Start Project → AI/MLRadar Target Classification with Deep Learning
Classify drones, birds, and aircraft from synthetic radar signatures
Train a CNN on synthetic radar range-Doppler maps to classify aerial targets (drone, bird, aircraft, clutter). Generate realistic synthetic data using radar cross-section models and signal processing fundamentals.
Start Project → AI/MLContrail Prediction and Avoidance with ML
Predict where contrails form and reroute flights to avoid them
Build an ML model predicting contrail formation probability from atmospheric conditions and satellite imagery. Explore how small route adjustments can significantly reduce aviation's non-CO2 climate impact.
Start Project → AI/MLPredict Pilot Fatigue Risk from Flight Schedule Data
Model the invisible threat to flight safety with ML
Model cumulative fatigue using the SAFTE/FAST framework, then train an ML model on schedule features to predict fatigue risk scores. Tackle one of aviation safety's most challenging human factors problems.
Start Project → AI/MLML-Based Gust Load Prediction for Wing Structures
Replace expensive time-domain simulations with a trained sequence model
Train a sequence model (LSTM or Transformer) on time-series gust encounter data to predict peak wing root bending moment. Compare against classical 1-minus-cosine gust analysis for a real-world loads problem.
Start Project → AI/MLNeural Network Surrogate for Thermal Protection System Design
Accelerate reentry vehicle thermal design with deep learning
Train a neural network on parametric FEA results to predict temperature distribution through a multi-layer thermal protection system for reentry vehicles. Use the surrogate for rapid design-space exploration.
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