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Raymond Lau

Publications and source records attributed to Raymond Lau.

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Development of Anisotropic Magnetized Viscosity for Magnetized Liner Inertial Fusion Simulations in FLASH

Magnetized liner inertial fusion (MagLIF) operates in a regime where anisotropic transport phenomena fundamentally influence implosion dynamics. In strongly magnetized plasmas, the viscous stress tensor becomes highly anisotropic, yet no prior work has incorporated or examined magnetized viscosity effects in MagLIF configurations. We present the first implementation of the full Braginskii magnetized viscosity tensor for arbitrary magnetic field orientations in the Pacific Fusion branch of FLASH. The implementation is verified through analytical comparisons, direct verification against Braginskii's original formulation, Method of Manufactured Solutions, and against analytical shock solutions. Application to MagLIF-relevant configurations reveals that magnetized viscosity damps vortical structures, converts kinetic energy in those vortical structures into thermal energy, and mitigates the Rayleigh-Taylor instabilities. Simulations with seeded perturbations demonstrate yield preservation when magnetized viscosity is included. These results establish magnetized viscosity as a non-negligible physical mechanism in MagLIF plasmas and provide a validated capability for predictive modeling of magnetized high-energy-density plasmas.

physics.plasm-ph

Applying Maxi-adjustment to Adaptive Information Filtering Agents

Learning and adaptation is a fundamental property of intelligent agents. In the context of adaptive information filtering, a filtering agent's beliefs about a user's information needs have to be revised regularly with reference to the user's most current information preferences. This learning and adaptation process is essential for maintaining the agent's filtering performance. The AGM belief revision paradigm provides a rigorous foundation for modelling rational and minimal changes to an agent's beliefs. In particular, the maxi-adjustment method, which follows the AGM rationale of belief change, offers a sound and robust computational mechanism to develop adaptive agents so that learning autonomy of these agents can be enhanced. This paper describes how the maxi-adjustment method is applied to develop the learning components of adaptive information filtering agents, and discusses possible difficulties of applying such a framework to these agents.

cs.AI