  As modern electronic devices are increasingly miniaturized and integrated,
their performance relies more heavily on effective thermal management.
Two-phase cooling methods enhanced by porous surfaces, which capitalize on
thin-film evaporation atop structured porous surfaces, are emerging as
potential solutions. In such porous structures, the optimum heat dissipation
capacity relies on two competing objectives that depend on mass and heat
transfer. The computational costs of evaluating these objectives, the high
dimensionality of the design space which a voxelated microstructure
representation, and the manufacturability constraints hinder the optimization
process for thermal management. We address these challenges by developing a
data-driven framework for designing optimal porous microstructures for cooling
applications. In our framework we leverage spectral density functions (SDFs) to
encode the design space via a handful of interpretable variables and, in turn,
efficiently search it. We develop physics-based formulas to quantify the
thermofluidic properties and feasibility of candidate designs via offline
simulations. To decrease the reliance on expensive simulations, we generate
multi-fidelity data and build emulators to find Pareto-optimal designs. We
apply our approach to a canonical problem on evaporator wick design and obtain
fin-like topologies in the optimal microstructures which are also
characteristics often observed in industrial applications.
