A Mass-Conserving-Perceptron for Machine Learning-Based Modeling of Geoscientific Systems

arXiv:2310.08644v3 Announce Type: replace
Abstract: Although decades of effort have been devoted to building Physical-Conceptual (PC) models for predicting the time-series evolution of geoscientific systems, recent work shows that Machine Learning (ML) based Gated Recurrent Neural Network technology can be used to develop models that are much more accurate. However, the difficulty of extracting physical understanding from ML-based models complicates their utility for enhancing scientific knowledge regarding system structure and function. Here, we propose a physically-interpretable Mass Conserving Perceptron (MCP) …

abstract arxiv building cs.ai cs.lg evolution machine machine learning modeling network neural network perceptron recurrent neural network series shows systems technology type work