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No. The only way to extrapolate statistically is to represent the problem in such a way that it is interpolating instead. This requires some form of prior knowledge or constraint
The only form of generalization you can do with a parametric curve (like a DL model) is interpolation, by construction. The way DL solves "extrapolative" problems is by first turning them into interpolative problems, which requires a dense sampling of the data manifold...
To do actual extrapolation from a few samples, you need a discrete model (like a computer program), not just a parametric curve. A simple example you can play with to understand the difference is list sorting: try to solve it either via program synthesis or via a DL model.
You can use 2-3 ex to synthesis a discrete program that will work with any possible list of ints, in any range, of any length. With DL, you'll need to first make the problem interpolative: train on millions of lists, then can only generalize to lists of similar length / ranges
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