Aerospace Researcher · Hypersonics · ML · Quantum

Predicting the unpredictable.

I'm Neil Akhawat — an aerospace researcher working at the intersection of hypersonic flight, machine learning, and quantum mechanics. My current focus is real-time trajectory prediction for hypersonic glide vehicles: estimating where a Mach–15 vehicle will be when classical tracking breaks down.

View research → About me ✉ Subscribe

Focus

Hypersonic trajectory prediction

Fields

Aerospace · ML · Quantum

Writing

neilakhawat.com

Video

@neilakhawat

NEIL AKHAWAT. Aerospace Researcher · Hypersonics · ML · Quantum

01 · Who I am

About me

An aerospace researcher working on the hardest things to predict — vehicles that evade prediction, systems that learn, and a universe that is uncertain at its core.

Neil Akhawat

Aerospace Researcher

FOCUSHypersonic trajectory prediction

FIELDSAerospace · ML · Quantum

WRITINGneilakhawat.com

VIDEO@neilakhawat

I'm Neil Akhawat, an aerospace researcher fascinated by the boundary between what we can compute and what we can never quite pin down. My work sits where aerospace, machine learning, and quantum mechanics meet — three fields bound together by a single question: how do you predict something that resists being predicted?

Right now that question takes a very concrete form. A hypersonic glide vehicle flies at Mach 5–25, maneuvers on its way down, and wraps itself in plasma that blinds radar at exactly the moment tracking matters most — turning trajectory estimation through that blackout window into one of the hardest open problems in aerospace tracking.

Around the core research, I write to make hard ideas legible — from missile flight regimes to Heisenberg's uncertainty principle — and I make video explainers that break these topics down for anyone curious about how the physical and computational world actually works.

I believe the most interesting problems live at the edge of certainty, and I'm building the tools — and the intuition — to work there.

Hypersonics Trajectory prediction State estimation Physics-informed ML Sequence models Quantum mechanics Scientific writing

My research → Get in touch

02 · What I work on

Research areas

My work spans three connected questions: how do we predict the path of a vehicle that actively evades prediction, how do learning systems model physical processes, and how do we reason under fundamental uncertainty. The common thread is prediction under hard constraints.

● Ongoing

Machine Learning · AI

Sequence models & learning under structure

How do recurrent and attention-based models learn temporal patterns — and how do we keep them physically honest? I work with dual-channel recurrent networks, sparse causal attention, and physics-residual training so that learned predictions never violate the laws governing the system they model.

Bi-GRU / LSTM Sparse attention Physics-residual loss

● Exploratory

Quantum Mechanics

Uncertainty & quantum machine learning

From the Heisenberg uncertainty principle to the question of how quantum systems can accelerate learning — I explore where quantum mechanics meets computation, and what it means to predict in a world that is fundamentally probabilistic at its base.

Uncertainty principle Quantum ML Probabilistic reasoning

● Foundational

Missile Systems · Flight Dynamics

Missile trajectories & flight regimes

The groundwork for the hypersonic research: understanding how ballistic, cruise, and glide missiles differ by their trajectories, the physics of each flight regime, and why each demands a different approach to tracking and interception.

Ballistic vs glide Flight dynamics Trajectory analysis

03 · Community

Aerospace × Artificial Intelligence Discussions

Ask a question, answer someone else's, and dig into the ideas behind flight, learning, and the physics underneath. Search first — your question may already have an answer, or browse the full Q&A library.