  All fields of science depend on mathematical models. Occam's razor refers to
the principle that good models should exclude parameters beyond those minimally
required to describe the systems they represent. This is because redundancy can
lead to incorrect estimates of model parameters from data, and thus inaccurate
or ambiguous conclusions. Here, we show how deep learning can be powerfully
leveraged to address Occam's razor. FixFit, our new method, uses a feedforward
deep neural network with a bottleneck layer to characterize and predict the
behavior of a given model from its input parameters. FixFit has three major
benefits. First, it provides a metric to quantify the original model's degree
of complexity. Second, it allows for the unique fitting of data. Third, it
provides an unbiased way to discriminate between experimental hypotheses that
add value versus those that do not. In two use cases, we demonstrate the broad
applicability of this method across scientific domains. To validate the method
using a known system, we apply FixFit to recover known composite parameters for
the Kepler orbit model. To illustrate how the method can be applied to less
well-established fields, we use it to identify parameters for a multi-scale
brain model and reduce the search space for viable candidate mechanisms.
