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On Shape Optimization with Large Magnetic Fields in Two Dimensions
Vladimir Lotoreichik1, Léo Morin2
1Department of Theoretical Physics, Nuclear Physics Institute, Czech Academy of Sciences, 25068 Řež, Czech Republic.
In strong magnetic fields, optimal domains for magnetic Laplacians exhibit symmetry. This study proves that domains with lower magnetic eigenvalues than a disk approach symmetry as magnetic fields strengthen.
Area of Science:
- Mathematical Physics
- Spectral Theory
- Differential Geometry
Background:
- Eigenvalues of magnetic Laplacians are crucial in understanding quantum systems and wave phenomena.
- Domain geometry significantly influences spectral properties, particularly under external fields.
- Symmetry principles often emerge in optimal configurations within physical systems.
Purpose of the Study:
- To investigate the relationship between domain shape and magnetic eigenvalues in the strong magnetic field limit.
- To establish asymptotic bounds for magnetic eigenvalues and their connection to domain asymmetry.
- To demonstrate that optimal domains for magnetic Laplacians tend towards symmetric shapes.
Main Methods:
- Derivation of several asymptotic bounds on magnetic eigenvalues.
- Analysis of the magnetic Dirichlet Laplacian for planar domains and rectangles.
- Investigation of the magnetic Dirac operator with infinite mass boundary conditions.
- Estimation of the torsion function on rectangles.
Main Results:
- Established that for a bounded simply-connected planar domain, if the n-th eigenvalue of the magnetic Dirichlet Laplacian is less than that of a disk of equal area, its Fraenkel asymmetry approaches zero in the strong magnetic field limit.
- Extended comparable results to rectangles and smooth domains with the magnetic Dirac operator.
- Provided a novel estimate for the torsion function on rectangular domains.
Conclusions:
- The study confirms that in the presence of strong magnetic fields, domains that optimize magnetic eigenvalues tend to exhibit increased symmetry.
- Asymptotic eigenvalue analysis provides a powerful tool for understanding geometric spectral properties.
- The findings have implications for spectral optimization problems in mathematical physics.
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