  In symbolic regression, the goal is to find an analytical expression that
accurately fits experimental data with the minimal use of mathematical symbols
such as operators, variables, and constants. However, the combinatorial space
of possible expressions can make it challenging for traditional evolutionary
algorithms to find the correct expression in a reasonable amount of time. To
address this issue, Neural Symbolic Regression (NSR) algorithms have been
developed that can quickly identify patterns in the data and generate
analytical expressions. However, these methods, in their current form, lack the
capability to incorporate user-defined prior knowledge, which is often required
in natural sciences and engineering fields. To overcome this limitation, we
propose a novel neural symbolic regression method, named Neural Symbolic
Regression with Hypothesis (NSRwH) that enables the explicit incorporation of
assumptions about the expected structure of the ground-truth expression into
the prediction process. Our experiments demonstrate that the proposed
conditioned deep learning model outperforms its unconditioned counterparts in
terms of accuracy while also providing control over the predicted expression
structure.
