Computing high-order mixed derivatives in physics-informed neural networks using multi-index Bell polynomials

Physics-informed neural networks for high-order partial differential equations require mixed input derivatives and their gradients with respect to the network parameters. Standard implementations obtain an order-$K$ derivative by repeated automatic differentiation. We instead organize the forward re