In previous publications, we have reported a computational approach to constructing quantitative process models of dynamic systems from time-series data and background knowledge. However, our experience with these systems suggests that process knowledge is insufficient to avoid the consideration of implausible models. To this end, we have identified and introduced constraints that specify which processes can or must occur together, which in turn limit search to candidates that scientists will consider acceptable. We have also developed methods for inducing such constraints from the results of search through the model space. We maintain that the ability to specify, utilize, and induce constraints on quantitative process models will support deeper understanding of complex systems and constitutes an important addition to eScience. For the past few years, we have been developing computational tools to aid scientists in constructing process models of complex systems. This work integrates ideas from artificial intelligence, simulation environments, machine learning, and human–computer interaction to represent, simulate, and discover models that explain observations while remaining consistent with knowledge about a domain. We believe this research fills an important need within the eScience movement.