Abstract Machine learning is playing an increasing role in the physical sciences and significant progress has been made towards embedding domain knowledge into models. Less explored its use to discover interpretable laws from data. We propose parsimonious neural networks (PNNs) that combine with evolutionary optimization find models balance accuracy parsimony. The power versatility of approach ...