###### Abstract
This study explored the performance of Gyroid, Diamond, and IWP in comparison with traditional fin structures, and the Diamond was found to be the best in heat transfer efficiency due to its unique design of lacking \"through-holes\" [26]. In another study, Schwartz P was found to be more efficient than Gyroid and Diamond in heat transfer and achieving lower pressure drop [27]. The simulation was done in Ansys Fluent, and the results were compared to conventional liquid-cooled radiators. Ansys Fluent was also used to simulate TPMS structures to improve the performance of glass microfiber-based thermal transpiration pumps [28]. A numerical simulation model and experimental  
Figure 1: TPMS structures and their respective equations that are available as built-in design in nTop. Regenerated from [21]. validation were performed on TPMS structures demonstrating enhanced heat transfer with improved thermal performance compared to conventional designs. New TPMS models outperform Diamond and Gyroid models by having superior convective heat transfer coefficients [29]. Another study among porous TPMS structures shows that the Gyroid performs better than Diamond and FKS exhibiting the highest Nusselt number for heat removal [30]. Computation analysis of Fisher-Koch (FK) TPMS was performed which showed their superior thermal-hydraulic performance compared to Schwartz-D [31]. This study also concludes that TPMS application significantly enhances heat exchanger design and performance. Conducting a thermal simulation on the established TPMS models provided the necessary data on the temperature difference between the cold base and the warm top surface of the structures to conduct further evaluation by data analysis. Regression analysis and multi-objective optimization: Regression analysis is a statistical tool for the investigation of relationships between variables [32; 33]. The responses are the predicted outcomes that are dependent on the factors or variables. A study employs this analysis to accurately predict different mechanical properties of TPMS lattice structures [34] such as mass, yield stress, elastic modulus, specific energy absorption, and onset of densification. Understanding the process of regression analysis is crucial since it has been widely used in many different fields such as cardiorespiratory fitness [35]. The simplest mathematical formulation of multi-objective optimization is to rank the candidate alternatives from the best to the worst while considering trade-offs between two or more conflicting criteria [36]. While calculating these appraisal scores, information concerning weights (relative importance) allocated to different criteria may be sometimes required. These criteria weights can be estimated subjectively taking into consideration the opinions of the participating experts or determined objectively based on the randomness/variability of the data in the decision matrix itself. A streamlined Taguchi approach with weighted factors was considered for multi-objective optimization to identify optimal process parameters [37]. The parameters were for the nano-powder-mixed electrical discharge machining of INCONEL800 that utilizes a copper electrode where the goal was to minimize surface roughness and maximize material removal rate. Another study applied multi-objective optimization to minimize printing time with higher-quality printed parts for ABS polymer in Fused Filament Fabrication [38]. Energy consumption, quality, and production responses were optimized simultaneously assigning weights to energy consumption responses, surface roughness, and material removal rate during the rough turning operation [39]. The Taguchi method was used in another study to identify the optimal factors such as temperature, catalyst concentration, and acidity of catalyst and their interactions that affect the thermocatalytic degradation of waste polypropylene to liquid fuel in a batch reactor [40].