AI-designed heat shields for reusable spacecraft, accepted at an IEEE conference

Every spacecraft that returns to Earth depends on a heat shield surviving re-entry. Arjun used machine learning to predict how fibre-reinforced composites perform under extreme heat, drawing on NASA's TPSX materials data covering density, thermal conductivity, heat capacity and a thermal shielding efficiency index. Random Forest and neural network models identified LI-900 and carbon-phenolic as the strongest combinations for insulation, pointing to a faster way to select and design materials for reusable spacecraft.

FIELDAerospace Materials

RESULTFirst-author paper accepted at an IEEE conference

VENUEIEEE, 2026

BEFORE THE FELLOWSHIP

A student at the National Academy for Learning in Bangalore fascinated by space travel, with no research experience in materials science.

AFTER

First author of an AI-driven study of thermal protection materials for spacecraft re-entry, accepted at an IEEE conference.

THE LEDGER

01First-author paper accepted at an IEEE conference

02Built on NASA TPSX extended composite materials data

03Identified LI-900 and carbon-phenolic as optimal insulation combinations

04Applied Random Forest and multi-layer perceptron models to materials design

NEXT CASE STUDYBen Zhang, First-author paper accepted at an IEEE conference

Every case study starts with one application.

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