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Aerospace engineering meets applied AI.

I’m Allan Olivito, an aerospace engineer exploring how machine learning and AI can be applied across the aerospace field. Here I share what I learn and turn it into practical engineering projects.

This site follows the process behind each project: the problem, the engineering decisions, the implementation, and what I learned along the way.

The applications may span different aerospace disciplines. The common thread is using engineering fundamentals, simulation, and data-driven tools to understand and build better aerospace systems.

Latest Articles

Breaking Navier-Stokes: how OpenAI solved a Millennium Prize Problem in 100 hours
·7 mins
An AI-generated proof could resolve a Millennium Prize Problem. What the proposed Navier-Stokes breakdown means, why a negative answer matters, and what remains open.
CanSat: Bringing an egg back from space with a paraglider
How we navigated flexible-wing aerodynamics, deciphered literature nuances, deduced missing inlet angles from photos, engineered millimeter-precise Kevlar rigging, and ran drone descent tests to fulfill a demanding CanSat mission requirement.
How to explain orbital decay to a linear regression
A satellite in low orbit is slowly falling, and there is a classical formula that predicts it. I wanted to know whether the simplest model in machine learning could do better, and the answer turned out to depend entirely on what I chose to tell it.
From zero to a 6-DoF simulator for LEO satellites
How I went from knowing almost no orbital mechanics to researching, designing, implementing, and verifying a six-degree-of-freedom satellite simulator.
Why I opened this space
A place to develop ideas, apply what I learn, and share aerospace engineering projects and experiences.