Computer simulation of fluids, thermal energy, and radiation enable rapid design and material testing. Carne Lab investigates numerical simulation methods for radiative heat transfer, particularly for coupled thermal-fluid problems.

Radiative transport simulations are computationally expensive, limiting our abilities to perform material discovery and optimization. Machine learning surrogate models, trained on Monte Carlo simulations, enable up to 1000-fold acceleration.

Radiative cooling provides passive cooling through the combination of ultra-high solar reflectance and thermal emission to deep space. This can be achieved at scale and low cost through radiative cooling paint formulations.