Article in progress. Leave your comments for debate. Will try to integrate later in the main body.
The more I learn on machine learning algorithms, putting in practice advanced applications of neural networks, deep learning convolutional networks, generative adversarial networks, recurrent neural networks and the like, the more I find similarities between these data-driven models and physics modeling.
My view is that (i) physics-based models have stand the test of time (centuries) and still will; (ii) data-driven models successes have been hyped because of the novelty of new algorithms and faster, ubiquitous computer power; (iii) some of the data-driven “everything” wave has been put forward with commercial interest in mind; (iv) the successes of data-driven models have been caused by a profound gap in physics-based modeling applications and software, -the “data wave” almost totally drowned to death to the physics-based modeling world; (v) physics-based modeling software neglected the effect of the continuous stream of data and chose to stay in the comfortable paradigm of charging for licenses; (vi) commercial physics-based modeling software underestimated the value brought by statistics, data science and machine learning; (vii) traditional physics-based modeling software companies have started to react but still don’t get it, -they have preferred to rename their products to *“any-word-here” + “intelligent”* to transmit their clients they have caught with the times of “artificial intelligence”.