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Author

Chang-Hong Mou

4 papers indexed here

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Preprint Sep 2026

Finance-Informed Operator Learning for Option Pricing with Quantum-Compatible Realizations

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, Yu Jiang et al. · 0 citations
Preprint Sep 2026

Finance-Informed Operator Learning for Option Pricing with Quantum-Compatible Realizations

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
Preprint Sep 2026

Stochastic Physics-Constrained Operator Inference for Complex Systems

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...

Chang-Hong Mou · 0 citations
#machine learning Preprint May 2026

PACE-FNO: Physics-Aligned Canonical Equivariance for Fourier Neural Operators

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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