Weighted universal approximation of differentiable maps on infinite-dimensional manifolds

• URL: http://arxiv.org/abs/2606.09820v1
• Date: Mon, 08 Jun 2026 17:57:40 GMT
• Title: Weighted universal approximation of differentiable maps on infinite-dimensional manifolds
• Abstract summary: We generalize the universal approximation theorem for functional input neural networks to differentiable maps.<n>A FNN maps the input from a possibly infinite-dimensional weighted manifold to the real-valued hidden layer.<n>We show that linear functions of the signature are able to approximate path space functionals including their directional derivatives.
• Score: 1.552674656890411
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: We generalize the universal approximation theorem for functional input neural networks (FNN) to differentiable maps by including the approximation of the derivatives. A FNN maps the input from a possibly infinite-dimensional weighted manifold to the real-valued hidden layer, on which a non-linear scalar activation function is applied, and then returns the output into a Banach space via some linear readouts. By proving a weighted Nachbin theorem, we establish a universal approximation theorem (UAT) for differentiable maps, which goes beyond the usual formulation on compact sets and also includes the approximation of the derivatives. This leads us to approximation results for non-anticipative functionals including the horizontal and vertical derivatives. As a further application, we show that linear functions of the signature are able to approximate path space functionals including their directional derivatives.

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