Modern computational methods involving highly sophisticated mathematical formulations enable several tasks like modeling complex physical phenomena, predicting key properties, and optimizing design. The higher fidelity in these computer models makes it computationally intensive to query them hundreds of times for optimization. One usually relies on a simplified model, albeit at the cost of losing predictive accuracy and precision. Towards this, data-driven surrogate modeling methods have shown much promise in emulating the behavior of expensive computer models. However, a major bottleneck in such methods is the inability to deal with high input dimensionality and the need for relatively large datasets. In certain cases, the high dimensionality of the input space can be attributed to its image-like characteristics, for example, the stress and displacement fields of continuums. With such problems, the …