We introduce an algorithm for combining two sources of similar data within a loss function to use in an optimisation procedure. The technique involves training model parameters on a ratio of two data sources to encourage convergence on meta-model parameters that map behaviours learned from each data source. The model can achieve higher quality results when unknown error distorts the signal of either source. Using a differentiable loss function we use gradient descent to optimise the hyperparameter which controls the ratio of data signals while training the model weights. We derive the theoretical bounds of the loss function based on the potential error of the meta-model, and the loss function is numerically interpreted to show the solution space. To utilise the methodology, we show its use in an example optimisation algorithm. Using this algorithm, we conducted an experimental case study to train functionapproximating surrogate models of the simple diffusion equation and the nonlinear Schrödinger equation. Thus, we show the ratio-coupled loss function may be utilised in approximating linear and nonlinear partial differential equations.