A finance-informed Deep Operator Network (FI-DeepONet) is proposed that decomposes the pricing operator into an input-dependent Black-Scholes carrier and a learned correction, and a quantum-compatible realization in which selected linear maps are exactly compiled or approximated by a restricted diagonal-orthogonal fami...
Jia-Rui Feng, Bing-Yang Hu, Yu Jiang et al.· 0 citations
Pricing European options under local volatility requires repeatedly solving a PDE whose coefficients change with recalibration, while practitioners need both prices and sensitivities across spot-time surfaces. Neural surrogates can amortize these solves, but near expiry the solution loses regularity, making curvature d...
Jia-Rui Feng, Bing-Yang Hu, Jiang-Wei Yu et al.· 0 citations
Quadratically nonlinear systems driven by additive noise arise throughout science and engineering, both as stochastic dynamical systems in their own right and as reduced-order models (ROMs) of turbulent flows. We propose stochastic physics-constrained operator inference (SPOpInf), a non-intrusive method that learns a s...
Neural operators are often tested on states that differ physically from training data. A distinct failure occurs when the physical dynamics are unchanged but the observed coordinate frame differs from training. PACE-FNO addresses this case by estimating the frame, predicting after pulling the field to a canonical repre...
Jia-Xi Xu, Chang-Hong Mou, Ye-Yu Zhang et al.· 1 citation
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