Data-driven construction of a generalized kinetic collision operator from molecular dynamics
We introduce a data-driven approach to learn a generalized kinetic collision operator directly from molecular dynamics. Unlike the conventional (e.g., Landau) models, the present operator takes an anisotropic form that accounts for a second energy transfer arising from the collective interactions between the pair of collision particles and the environment. Numerical results show that preserving the broadly overlooked anisotropic nature of the collision energy transfer is crucial for predicting the plasma kinetics with non-negligible correlations, where the Landau model shows limitations.
Code (0)
등록된 구현이 없습니다.
Similar Papers 제목 키워드 기반
Fast spectral separation method for kinetic equation with anisotropic non-stationary collision operator retaining micro-model fidelity
We present a generalized, data-driven collisional operator for one-component plasmas, learned from molecular dynamics simulations, to extend the collisional kinetic model beyond the weakly coupled regime. The proposed op…
Computational EfficiencyFast-Forward Lattice Boltzmann: Learning Kinetic Behaviour with Physics-Informed Neural Operators
The lattice Boltzmann equation (LBE), rooted in kinetic theory, provides a powerful framework for capturing complex flow behaviour by describing the evolution of single-particle distribution functions (PDFs). Despite its…
Hybrid Kinetics Embedding Framework for Dynamic PET Reconstruction
In dynamic positron emission tomography (PET) reconstruction, the importance of leveraging the temporal dependence of the data has been well appreciated. Current deep-learning solutions can be categorized in two groups i…
Kinetic-Diffusion-Rotation Algorithm for Dose Estimation in Electron Beam Therapy
Monte Carlo methods are state-of-the-art when it comes to dosimetric computations in radiotherapy. However, the execution time of these methods suffers in high-collisional regimes. We address this problem by introducing …
KO: Kinetics-inspired Neural Optimizer with PDE Simulation Approaches
The design of optimization algorithms for neural networks remains a critical challenge, with most existing methods relying on heuristic adaptations of gradient-based approaches. This paper introduces KO (Kinetics-inspire…
Diversityimage-classificationImage Classificationtext-classification+1