Learning Monotone Dynamics by Neural Networks. (arXiv:2006.06417v2 [cs.LG] UPDATED)

Feed-forward neural networks (FNNs) work as standard building blocks in
applying artificial intelligence (AI) to the physical world. They allow
learning the dynamics of unknown physical systems (e.g., biological and
chemical) {to predict their future behavior}. However, they are likely to
violate the physical constraints of those systems without proper treatment.
This work focuses on imposing two important physical constraints: monotonicity
(i.e., a partial order of system states is preserved over time) and stability
(i.e., the system states converge over …

arxiv dynamics learning lg networks neural networks