Faster Thermal Profiling of a Lunar Rover with Machine Learning Adapted Finite Difference Model

ArXi:2605.27651v1 Announce Type: new Autonomous space systems operating in extreme thermal environments require accurate and efficient thermal modeling to both pre-mission system design and onboard autonomy. For lunar rovers, large temperature gradients, radiative heat transfer, and variable surface conditions make reliable thermal prediction especially challenging. High-fidelity physics-based simulations provide accurate results but are computationally expensive, while simplified models and lookup-table approach often lack sufficient accuracy.