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Explore color confinement, flux tubes, and large N gauge theories in this talk on worldsheet theory of confining flux tubes, combining perspectives from various fields of physics.
Explores 1-form symmetries and phase diagrams in Yang-Mills theory, discussing theta angles, lattice perspectives, and line defects to provide insights into color confinement and gauge theory dynamics.
Explore the latest findings on (g-2)m from lattice QCD and experiments, including the 4.2 sigma discrepancy, experimental results, and theoretical contributions in this comprehensive physics talk.
Explores flux tube spectrum and world-sheet axion in color confinement, combining lattice simulations, string theory, and gauge/gravity duality to study non-perturbative phenomena in non-Abelian gauge theories.
Explore stellar structure, transport mechanisms, and climate through global oscillation modes and chemical abundances. Gain insights into observational techniques and numerical challenges in astrophysics.
Explore ocean physics in climate change, focusing on sea level patterns and improving climate simulations through machine learning and physics integration for more accurate projections.
Exploring the physics behind morphogenesis and how living matter shapes itself, focusing on recent developments in understanding complex biological transformations and organ development.
Exploring convective penetration in stellar interiors through theory and simulations, focusing on transport processes and their impact on stellar physics and modeling.
Explore stellar interiors through asteroseismology, examining pulsation modes, rotation rates, and internal mixing processes. Gain insights into stellar physics using observational data and theoretical models.
Explores rotating star-planet system architectures using data analysis, machine learning, and modeling techniques. Covers hot stars, masking, decision trees, random forests, and orbital period observations.
Comprehensive overview of 1D stellar interior modeling, covering key physics, observational challenges, and recent advancements. Explores transport processes, boundary conditions, and calibration techniques for improving stellar physics understanding.
Explore machine learning applications in Earth system modeling, from parameter calibration to feature detection, enhancing climate predictions and understanding of complex environmental processes.
Explores using neural networks to improve coarse-scale climate models, addressing unresolved scales and enhancing predictions. Discusses framework, wavelet analysis, and multi-model architecture for more accurate climate simulations.
Explores deep learning parameterization of gravity wave drag in climate models, demonstrating improved predictions of atmospheric phenomena like the QBO and potential for generalizing to future climate scenarios.
Exploring machine learning for atmospheric physics parameterization, focusing on cloud superparameterization emulation. Discusses challenges, strategies, and lessons learned in applying ML to climate modeling.
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