Machine learning (ML) has found immense success in commercial applications such as computer vision and natural language processing. Given the success of ML in commercial domains, there is an increasing interest in using ML models for advancing scientific discovery. However, direct application of ``black-box" ML models has met with limited success in scientific domains given that the data available for many scientific problems is far smaller than what is needed to effectively train advanced ML models. Additional challenge arises due to the data non-stationarity in space and time. In the absence of adequate information about the physical mechanisms of real-world processes, ML approaches are prone to false discoveries of patterns which look deceptively good on training data but cannot generalize to unseen scenarios
