  Partial differential equations (PDEs) are ubiquitous in the world around us,
modelling phenomena from heat and sound to quantum systems. Recent advances in
deep learning have resulted in the development of powerful neural solvers;
however, while these methods have demonstrated state-of-the-art performance in
both accuracy and computational efficiency, a significant challenge remains in
their interpretability. Most existing methodologies prioritize predictive
accuracy over clarity in the underlying mechanisms driving the model's
decisions. Interpretability is crucial for trustworthiness and broader
applicability, especially in scientific and engineering domains where neural
PDE solvers might see the most impact. In this context, a notable gap in
current research is the integration of symbolic frameworks (such as symbolic
regression) into these solvers. Symbolic frameworks have the potential to
distill complex neural operations into human-readable mathematical expressions,
bridging the divide between black-box predictions and solutions.
