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Climate models exhibit significant biases in simulating present‐day tropical Pacific sea surface temperature (SST) patterns, particularly the zonal SST gradient, which may contribute to uncertainties in precipitation projections over mid‐latitude populated regions. Biases in the simulated tropical Pacific SST gradient across CMIP6 models significantly influence present‐day and future winter precipitation over South America through a stationary wave pattern resembling the Pacific–South American (PSA‐2) mode. Models with a weaker‐than‐observed SST gradient simulate a deeper trough east of South America, resulting in stronger wetting trends over northern Argentina. Applying observational constraints reduces uncertainties in projected precipitation trends by approximately 31%. For Tasmania and New Zealand, SST gradient biases impact the simulation of present‐day winter precipitation, but are not well correlated with future precipitation projections. Our findings highlight the critical need to accurately represent the tropical Pacific SST gradient and its associated atmospheric circulation features for reliable regional climate simulation. 

This work presents the transient temperature measurement and modeling of thermochromic variable emitters using a lab-scale cryothermal vacuum test setup. A cryostat is used to provide a space-like environment with a high vacuum and an 80 K heat sink, while a custom-designed sample holder is employed to heat up the sample with transient temperature measurement. Validation with a tungsten mirror is conducted with careful calibration of heat losses as a function of sample temperature. Approaches to reduce the heat losses are discussed as well. A previously fabricated variable emitter made of thermochromic [Formula: see text] thin film in a Fabry–Perot nanophotonic structure, whose infrared emittance increases with temperature upon [Formula: see text] insulator-to-metal phase transition, is experimentally tested at different heating power inputs. A transient heat transfer model is also developed to validate the measurements, and a thermal homeostasis effect with reduced temperature swing from the variable emitter is predicted in comparison to a commonly used static emitter. This novel cryothermal vacuum test platform would facilitate the lab-scale thermal testing of novel variable-emittance coatings for space heat control applications. 

Thermochromic vanadium dioxide thin films have attracted much attention recently for constructing variable-emittance coatings upon their insulator-metal phase transition for dynamic thermal control. However, fabrication of high-quality vanadium dioxide thin films in a cost-effective way is still a challenge. In addition, the phase transition temperature of vanadium dioxide is around 68 °C, which is higher than most of terrestrial and extraterrestrial applications. In this study, we report the fabrication and characterization of tungsten-doped vanadium dioxide thin films with lowered phase transition temperatures via co-sputtering, furnace oxidation, and thermal annealing processes for wider application needs. Doping is achieved by co-sputtering of tungsten and vanadium targets while the doping level is varied by carefully controlling the sputtering power for tungsten. Doped thin film samples of 30 nm thick with different tungsten atomic concentrations are prepared by co-sputtering onto undoped silicon wafers. Optimal oxidation time of 4 h is determined to reach full oxidation in an oxygen-rich furnace environment at 300 °C. A systematic thermal annealing study is carried out to find the optimal annealing temperature and time. By using an optical cryostat coupled to an infrared spectrometer, the temperature-dependent infrared transmittance of fully annealed tungsten-doped vanadium dioxide thin films is measured in a wide temperature range from −60 to 100 °C. The phase transition temperature is found to decrease at 24.5 °C per at. % of tungsten doping, and the thermal hysteresis between heating and cooling shrinks at 5.5 °C per at. % from the fabricated vanadium dioxide thin films with tungsten doping up to 4.1 at. %. 

Abstract Artificial intelligence and machine learning frameworks have become powerful tools for establishing computationally efficient mappings between inputs and outputs in engineering problems. These mappings have enabled optimization and analysis routines, leading to innovative designs, advanced material systems, and optimized manufacturing processes. In such modeling efforts, it is common to encounter multiple information (data) sources, each varying in specifications. Data fusion frameworks offer the capability to integrate these diverse sources into unified models, enhancing predictive accuracy and enabling knowledge transfer. However, challenges arise when these sources are heterogeneous, i.e., they do not share the same input parameter space. Such scenarios occur when domains differentiated by complexity such as fidelity, operating conditions, experimental setup, and scale, require distinct parametrizations. To address this challenge, a two-stage heterogeneous multi-source data fusion framework based on the input mapping calibration (IMC) and the latent variable Gaussian process (LVGP) is proposed. In the first stage, the IMC algorithm transforms the heterogeneous input parameter spaces into a unified reference parameter space. In the second stage, an LVGP-enabled multi-source data fusion model constructs a single-source-aware surrogate model on the unified reference space. The framework is demonstrated and analyzed through three engineering modeling case studies with distinct challenges: cantilever beams with varying design parametrizations, ellipsoidal voids with varying complexities and fidelities, and Ti6Al4V alloys with varying manufacturing modalities. The results demonstrate that the proposed framework achieves higher predictive accuracy compared to both independent single-source and source-unaware data fusion models. 

Mechanical metamaterials represent an innovative class of artificial structures, distinguished by their extraordinary mechanical characteristics, which are beyond the scope of traditional natural materials. The use of deep generative models has become increasingly popular in the design of metamaterial units. The effectiveness of using deep generative models lies in their capacity to compress complex input data into a simplified, lower-dimensional latent space, while also enabling the creation of novel optimal designs through sampling within this space. However, the design process does not take into account the effect of model uncertainty due to data sparsity or the effect of input data uncertainty due to inherent randomness in the data. This might lead to the generation of undesirable structures with high sensitivity to the uncertainties in the system. To address this issue, a novel uncertainty-aware deep learning framework-based robust design approach is proposed for the design of metamaterial units with optimal target properties. The proposed approach utilizes the probabilistic nature of the deep learning framework and quantifies both aleatoric and epistemic uncertainties associated with surrogate-based design optimization. We demonstrate that the proposed design approach is capable of designing high-performance metamaterial units with high reliability. To showcase the effectiveness of the proposed design approach, a single-objective design optimization problem and a multi-objective design optimization problem are presented. The optimal robust designs obtained are validated by comparing them to the designs obtained from the topology optimization method as well as the designs obtained from a deterministic deep learning framework-based design optimization where none of the uncertainties in the system are explicitly considered.