Applying the physics-constrained machine learning approach to other complex many-body systems, such as biological systems, presents several limitations and challenges. One significant challenge is the inherent complexity and variability of biological interactions, which often involve a multitude of factors, including biochemical signaling, cellular mechanics, and environmental influences. Unlike dusty plasma systems, where certain physical laws can be approximated, biological systems may not have well-defined interaction laws, making it difficult to impose physical constraints on the ML models.
Additionally, the data acquisition process in biological systems can be more challenging due to the need for high-resolution imaging and tracking of dynamic processes, which may not be as straightforward as in controlled dusty plasma experiments. The presence of noise, variability in biological responses, and the difficulty in isolating specific interactions can complicate the training and validation of ML models.
Furthermore, the interpretability of the ML models becomes crucial in biological contexts, where understanding the underlying mechanisms is essential. The black-box nature of many ML algorithms may hinder the ability to derive meaningful insights from the model outputs, limiting their applicability in hypothesis-driven research.
Lastly, the scalability of the approach to larger biological systems, such as tissues or entire organisms, poses additional challenges. The combinatorial complexity of interactions increases significantly with the number of components, making it difficult to maintain model accuracy and interpretability.