Finding 2262Emerging EvidenceValidation V0
This paper innovatively applies physics-informed neural networks (PINNs) to the time-space-fractional Black-Scholes equation, incorporating the Grunwald-Letnikov derivative and a novel spatial operator transformation. Avoiding mesh discretization, it delivers robust European put option results. While building on recent advances, its unique computational approach significantly advances option pricing under fractional dynamics.
78%Confidence
1Evidence objects
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Supporting78% linkage confidence
This paper innovatively applies physics-informed neural networks (PINNs) to the time-space-fractional Black-Scholes equation, incorporating the Grunwald-Letnikov derivative and a novel spatial operator transformation. Avoiding mesh discretization, it delivers robust European put option results. While building on recent advances, its unique computational approach significantly advances option pricing under fractional dynamics.
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Inspect source: An Efficient Physics-Informed Neural Network Solution to the Time-Space Fractional Black-Scholes Equation →This Finding was extracted from the configured corpus. It is versioned, traceable, and may evolve through editorial review or new corpus evidence.