  Modeling thermal states for complex space missions, such as the surface
exploration of airless bodies, requires high computation, whether used in
ground-based analysis for spacecraft design or during onboard reasoning for
autonomous operations. For example, a finite-element thermal model with
hundreds of elements can take significant time to simulate, which makes it
unsuitable for onboard reasoning during time-sensitive scenarios such as
descent and landing, proximity operations, or in-space assembly. Further, the
lack of fast and accurate thermal modeling drives thermal designs to be more
conservative and leads to spacecraft with larger mass and higher power budgets.
The emerging paradigm of physics-informed machine learning (PIML) presents a
class of hybrid modeling architectures that address this challenge by combining
simplified physics models with machine learning (ML) models resulting in models
which maintain both interpretability and robustness. Such techniques enable
designs with reduced mass and power through onboard thermal-state estimation
and control and may lead to improved onboard handling of off-nominal states,
including unplanned down-time. The PIML model or hybrid model presented here
consists of a neural network which predicts reduced nodalizations (distribution
and size of coarse mesh) given on-orbit thermal load conditions, and
subsequently a (relatively coarse) finite-difference model operates on this
mesh to predict thermal states. We compare the computational performance and
accuracy of the hybrid model to a data-driven neural net model, and a
high-fidelity finite-difference model of a prototype Earth-orbiting small
spacecraft. The PIML based active nodalization approach provides significantly
better generalization than the neural net model and coarse mesh model, while
reducing computing cost by up to 1.7x compared to the high-fidelity model.
