Faster Thermal Profiling of a Lunar Rover with Machine Learning Adapted Finite Difference Model
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Computer Science > Machine Learning
arXiv:2605.27651 (cs)
[Submitted on 26 May 2026]
Title:Faster Thermal Profiling of a Lunar Rover with Machine Learning Adapted Finite Difference Model
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Abstract:Autonomous space systems operating in extreme thermal environments require accurate and efficient thermal modeling to support 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. Physics-informed machine learning (PIML) offers a promising alternative by combining data-driven models with embedded physical knowledge. This paper presents a PIML framework for thermal analysis of a simplified lunar rover with internal heat sources, where machine learning enables environment-adaptive coarse meshing. The proposed architecture integrates a transfer neural network (TNN) that adaptively determines 3D finite-difference nodalization based on thermal loads and initial conditions, enabling more accurate coarse-mesh calculations. A differentiable finite-difference thermal simulator is embedded within the framework to enforce physical consistency and support efficient training, while an upscaling layer reconstructs high-resolution temperature fields from the coarse-grid solution. The proposed PIML approach is evaluated against high-fidelity fine-mesh simulations, low-fidelity fixed coarse-mesh models, and a purely data-driven artificial neural network (ANN). Results show that the PIML framework improves prediction accuracy by 50% and 39% relative to the coarse-mesh physics model and ANN model, respectively, while maintaining physically consistent thermal distributions. Computationally, the framework is also 3x faster than high-fidelity simulations, demonstrating an effective balance between accuracy and efficiency for thermal modeling of lunar rover systems.
| Subjects: | Machine Learning (cs.LG) |
| Cite as: | arXiv:2605.27651 [cs.LG] |
| (or arXiv:2605.27651v1 [cs.LG] for this version) | |
| https://doi.org/10.48550/arXiv.2605.27651
arXiv-issued DOI via DataCite (pending registration)
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View a PDF of the paper titled Faster Thermal Profiling of a Lunar Rover with Machine Learning Adapted Finite Difference Model, by Samuel Weber and 1 other authors
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