  Purpose: We seek to use neural networks (NNs) to solve a well-known system of
differential equations describing the balance between T cells and HIV viral
burden.
  Materials and Methods: In this paper, we employ a 3-input parallel NN to
approximate solutions for the system of first-order ordinary differential
equations describing the above biochemical relationship.
  Results: The numerical results obtained by the NN are very similar to a host
of numerical approximations from the literature.
  Conclusion: We have demonstrated use of NN integration of a well-known and
medically important system of first order coupled ordinary differential
equations. Our trial-and-error approach counteracts the system's inherent scale
imbalance. However, it highlights the need to address scale imbalance more
substantively in future work. Doing so will allow more automated solutions to
larger systems of equations, which could describe increasingly complex and
biologically interesting systems.
