Exploring Foundational and Biophysical Models in Computation

A lab led by Andreas Tola has developed efficient and effective foundational models for training convolutional neural networks. This approach may also be applied to biophysical models, offering a better way to handle complexity. The goal is to create a generative model that is indistinguishable from reality, fooling even experts. However, it still raises the question of whether this model can predict new phenomena. These generative models work best when we have a deep understanding of the system's mode of operation and can explore a significant portion of its behavior.