2 papers
cs.LG2026
Physics-Informed Neural Networks for Solving Derivative-Constrained PDEs
Kentaro Hoshisashi, Carolyn E Phelan, Paolo Barucca
Physics-Informed Neural Networks (PINNs) recast PDE solving as an optimisation problem in function space by minimising a residual-based objective, yet many applications require add…
q-fin.CP2024
Whack-a-mole Online Learning: Physics-Informed Neural Network for Intraday Implied Volatility Surface
Kentaro Hoshisashi, Carolyn E. Phelan, Paolo Barucca
Calibrating the time-dependent Implied Volatility Surface (IVS) using sparse market data is an essential challenge in computational finance, particularly for real-time applications…