While learning biases can be effective in incorporating physical laws into machine learning models, there are potential limitations that need to be considered. One limitation is the risk of overfitting the model to the training data, especially when the weights between the data fitting and physics fitting terms are not appropriately balanced. If the weights are not carefully chosen, the model may prioritize fitting the training data too closely, leading to poor generalization on unseen data.
To address this limitation, it is essential to carefully tune the hyperparameters of the model, including the weights assigned to the data fitting and physics fitting terms. Cross-validation techniques can be employed to find the optimal balance between these terms and prevent overfitting. Additionally, regularization techniques, such as L1 or L2 regularization, can be applied to the loss function to prevent the model from becoming too complex and overfitting the training data.
Another potential limitation of using learning biases is the need for a deep understanding of the underlying physics to formulate the appropriate constraints. If the physical laws are not well-defined or understood, it can be challenging to encode them effectively into the machine learning model. In such cases, collaboration between domain experts and machine learning practitioners is crucial to ensure that the constraints accurately reflect the known physics of the system.