The proposal aims to revolutionize the engineering landscape by addressing the critical challenge of data scarcity.

In contrast to industries benefiting from vast datasets (e.g., Facebook), many engineering domains operate with limited, high-quality data, which often hinders the development of robust machine learning models. This proposal focuses on harnessing innovative data-efficient methodologies, including active learning, surrogate modeling, and few-shot learning, to maximize insights from minimal datasets. By integrating these advanced techniques, the project aims to enhance the design and optimization processes within engineering, ultimately reducing resource consumption and accelerating innovation.

Furthermore, the initiative will foster collaboration among researchers and industry professionals, facilitating knowledge exchange in data-efficient machine learning. Through this interdisciplinary approach, the project aspires to set new standards in engineering design, paving the way for smarter, more efficient solutions in various applications.