A comparison of Single- and Double-generator formalisms for Thermodynamics-Informed Neural Networks

arXiv:2404.01060v1 Announce Type: new
Abstract: The development of inductive biases has been shown to be a very effective way to increase the accuracy and robustness of neural networks, particularly when they are used to predict physical phenomena. These biases significantly increase the certainty of predictions, decrease the error made and allow considerably smaller datasets to be used.
There are a multitude of methods in the literature to develop these biases. One of the most effective ways, when dealing with physical …

abstract accuracy arxiv biases comparison cs.lg development error generator inductive networks neural networks predictions robustness type