A derivative-informed operator learning framework trains neural and random-feature surrogates to reproduce both prices and Fréchet derivatives, yielding lower Greek errors and hedging instability in Black-Scholes, Heston, and volatility-surface experiments.
Dimension reduction for derivative- informed operator learning: An analysis of approximation errors
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Proves first UATs for k-times differentiable nonlinear operators and their derivatives via OL architectures uniformly on compact sets in weighted Bastiani-Sobolev spaces on general Banach spaces.
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Derivative-Informed Operator Learning for Finance: On-the-Fly Greeks, Surfaces, Hedging, and Control
A derivative-informed operator learning framework trains neural and random-feature surrogates to reproduce both prices and Fréchet derivatives, yielding lower Greek errors and hedging instability in Black-Scholes, Heston, and volatility-surface experiments.
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Universal Approximation of Nonlinear Operators and Their Derivatives
Proves first UATs for k-times differentiable nonlinear operators and their derivatives via OL architectures uniformly on compact sets in weighted Bastiani-Sobolev spaces on general Banach spaces.