Mechanistic Model

Mechanistic models aim to represent the underlying causal mechanisms of a system, integrating prior knowledge with data-driven approaches to improve prediction accuracy and interpretability. Current research focuses on hybrid models combining differential equations with machine learning techniques like neural networks and Gaussian processes, often employing Bayesian inference or gradient descent for parameter estimation and uncertainty quantification. These advancements are impacting diverse fields, from biomanufacturing and drug development to climate modeling and robotics, by enabling more accurate predictions, efficient data use, and improved understanding of complex systems. The integration of mechanistic and data-driven approaches is a key trend, leading to more robust and reliable models across various scientific disciplines.