HKU co-hosts International Lunar Sample Research Symposium
Meeting Announcement
Updates every hour. Last Updated: 19-Dec-2025 09:11 ET (19-Dec-2025 14:11 GMT/UTC)
Abstract
Purpose – The primary objective of this research is to develop new algorithms in the framework of deep neural networks for the valuation of options under dynamics driven by stochastic volatility models. We aim to use the Heston model for equity options to demonstrate the accuracy of our approach.
Design/methodology/approach – Physics-informed neural networks (PINNs) are trained to minimize a loss function that includes terms from the partial differential equation residuals, initial condition and boundary conditions evaluated at selected points in the space-time domain. Speed and accuracy comparisons are carried out against single hidden-layer neural networks, called physics-informed extreme learning machines (PIELMs). American options are formulated as linear complementarity problems, and PINNs are applied in conjunction with penalty methods for the computation of the option prices.
Findings – For American options under the Heston model, PINNs yield accurate prices. Computed Greeks sensitivities are in close agreement with those reported for mesh-based methods. In contrast to mesh-based penalty methods for American options, PINNs work with smaller values of the penalty term. For the real estate index American option problem, numerical prices obtained using PINNs have comparable accuracies as those obtained by a high-order radial basis functions finite difference scheme.
Practical implications – There is a lack of reliable pricing models for pricing property derivatives. This work contributes to developing accurate neural network algorithms.
DAEJEON, SOUTH KOREA – A joint research team from the Korea Advanced Institute of Science and Technology (KAIST) and the Unmanned Exploration Laboratory (UEL) has developed a transformative wheel capable of navigating the Moon’s most extreme terrains, including steep lunar pits and lava tubes.DAEJEON, SOUTH KOREA – A joint research team from the Korea Advanced Institute of Science and Technology (KAIST) and the Unmanned Exploration Laboratory (UEL) has developed a transformative wheel capable of navigating the Moon’s most extreme terrains, including steep lunar pits and lava tubes.
Free-space optical communications (FSOC), which use lasers for high-speed data links between aircraft, spacecraft, and ground stations, are limited by size and power constraints. To overcome this, researchers from the Karlsruhe Institute of Technology, Germany, proposed and experimentally validated a fiber-bundle-based architecture that could enable compact, multi-directional FSOC.