Leveraging Machine Learning for Estimating Relationship Model Through Empirical Scientific Data

The quest for understanding and modeling complex scientific phenomena often relies on the formulation of mathematical equations that accurately describe observed relationships. It has been an acceptable practice that many models require scientists to throw in different possible equations that should be able to fit the empirical data from scientific experiments. However, the constants and their interaction with the independent variables used are often estimated. Their values are often obtained through theoretical calculations or empirical fitting processes. However, such traditional approaches can be time-consuming, error-prone, and limited in their ability to capture intricate patterns within the data. In recent years, machine learning techniques have emerged as a promising technique to expedite and optimize the process of identifying and estimating the models without coming up with these constants and their relationship with the independent variables by bypassing the error-prone empirical modeling process. This paper presents a comprehensive exploration of utilizing machine learning methodologies for finding relationship model from the utilization of empirical data. We discuss various techniques, challenges, and opportunities associated with leveraging machine learning algorithms to extract the best relationship model along with its constants and hyperparameters, ultimately enhancing the accuracy and applicability of mathematical models in scientific research.