Rigorous derivation of the 4-wave kinetic equation for the full beta-FPUT system in the joint limit N to infinity and beta to zero under weakly nonlinear scalings, reaching times up to the kinetic timescale to the power 2/3, by directly incorporating non-resonant terms in a diagrammatic expansion.
Hilbert’s sixth problem: derivation of fluid equations via B oltz- mann’s kinetic theory
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The Deng-Hani molecule reduction algorithm constructs a Kruskal spanning tree of the input molecule.
DustNET is proposed as a shared dataset to train machine learning models that complement traditional physics equations for predictive modeling of dusty plasmas across laboratory and natural scales.
In 1D lattice Schwinger and NJL models, single-particle momentum distributions dominate two-particle correlations once thermal kinetic energy becomes comparable to the interaction strength, supporting applicability of kinetic theory.
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Rigorous Derivation of the Wave Kinetic Equation for full $\beta$-FPUT System
Rigorous derivation of the 4-wave kinetic equation for the full beta-FPUT system in the joint limit N to infinity and beta to zero under weakly nonlinear scalings, reaching times up to the kinetic timescale to the power 2/3, by directly incorporating non-resonant terms in a diagrammatic expansion.
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Kruskal-style algorithm for cubic Schr\"odinger equation molecule reduction
The Deng-Hani molecule reduction algorithm constructs a Kruskal spanning tree of the input molecule.
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DustNET: enabling machine learning and AI models of dusty plasmas
DustNET is proposed as a shared dataset to train machine learning models that complement traditional physics equations for predictive modeling of dusty plasmas across laboratory and natural scales.
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Applicability of kinetic theory in strongly coupled thermal quantum systems
In 1D lattice Schwinger and NJL models, single-particle momentum distributions dominate two-particle correlations once thermal kinetic energy becomes comparable to the interaction strength, supporting applicability of kinetic theory.