Domains using JAX
Neural ODE Orbit Propagation with JAX
Let automatic differentiation learn orbital physics from trajectory data.
Use JAX's autodiff and ODE solvers to build a Neural ODE that learns satellite orbit dynamics purely from observed position/velocity data. You will compare the learned propagator against classical two-body and J2 models, and explore how the neural model captures unmodelled perturbations.
Start Project → AI/MLDifferentiable Airfoil Optimization with JAX
Use automatic differentiation to find the optimal airfoil shape via gradient descent
Implement a differentiable panel-method aerodynamics solver in JAX and use automatic differentiation to compute exact gradients of lift and drag with respect to airfoil shape parameters. Perform gradient-based optimization to find airfoils that maximize L/D for given flight conditions.
Start Project → AI/MLSimulate Gravity with JAX
Watch planets orbit each other in a simulation you built from scratch
Use JAX to build a simple gravity simulator that models how two or three bodies orbit each other. Learn the basics of numerical integration and automatic differentiation while creating animated orbital plots.
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