To advance global collaboration in lunar science, the Department of Earth and Planetary Sciences at The University of Hong Kong (DEPS-HKU) and the Institute of Geology and Geophysics, Chinese Academy of Sciences (IGG-CAS) jointly hosted the International Lunar Sample Research Symposium 2025 from 21 to 24 November. The event attracted 230 participants from China, the USA, France, the UK, Japan, India, and other regions around the world.

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.

The gravitational wave is a prediction of the general theory of relativity that can be detected by measuring the relative distance between two test masses (TM) along the geodesics. The TM is fixed by the cage and vent mechanism to avoid collision with the spacecraft cavity during launch. Once the drag-free spacecraft reaches its target orbit, the TM must be released by the grabbing positioning and release mechanism (GPRM) to the cage center of the inertial sensor in a limited amount of time. The capture of the TM by means of the electrostatic suspensions is crucial for the mission. However, the GPRM inevitably generates large release errors and the low electrostatic force makes the TM capture control a difficult task. Moreover, the experimental results of the LISA Pathfinder mission have shown that the TM release velocities were well higher than the ones expected. The previous robust sliding mode controller needs to be optimized.

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.

 Faraway planet PSR J2322-2650b defies our understanding of planet formation, say UChicago scientists