Student researcher · Aerospace · ML · Quantum

I study how things move, think, and cannot be known.

I'm Neil Akhawat — a student 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.

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Neil Akhawat

Student Researcher

FOCUSHypersonic trajectory prediction

FIELDSAerospace · ML · Quantum

WRITINGneilakhawat.com

VIDEO@neilakhawat

01 · 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.

● Active · Primary

Aerospace · Hypersonics · Defense

Hypersonic glide vehicle trajectory prediction

A hypersonic glide vehicle travels at Mach 5–25, maneuvers actively, and ionizes the air around it — disappearing from radar exactly when tracking matters most. My research builds a physics-constrained prediction pipeline that reconstructs a vehicle's hidden aerodynamic state from noisy radar and satellite data, forecasts its future maneuver intent rather than its raw position, and propagates trajectories through a digital twin built on hypersonic equations of motion and a standard atmosphere model. The goal is a calibrated confidence corridor for where the vehicle will be — generated from realistic sensor input alone, surviving plasma blackout.

IMM-UKF state estimation Intent prediction Plasma blackout modeling Physics-informed ML Anderson hypersonic EOM Monte Carlo corridors

● 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

02 · Writing