Scarce Data

Scarce data research focuses on developing robust machine learning models that can effectively learn from limited datasets, a common challenge across diverse scientific and engineering domains. Current research emphasizes techniques like self-supervised learning, multifidelity approaches, and the incorporation of prior knowledge or expert models to improve model accuracy and generalization. These advancements are crucial for applications where acquiring large datasets is expensive, time-consuming, or ethically problematic, impacting fields ranging from medical image analysis and materials science to financial forecasting and control systems. The ultimate goal is to enable reliable and trustworthy AI solutions even when training data is severely limited.