What is he best known for?
The fields of study he is best known for:
- Quantum mechanics
- Neutron
- Quantum field theory
Mean field theory, Quantum mechanics, Atomic physics, Neutron and Quantum electrodynamics are his primary areas of study.
His Mean field theory research is multidisciplinary, relying on both Symmetry breaking, Hartree, Angular momentum, Spectral line and Nuclear drip line.
Jie Meng combines subjects such as Nuclear theory and Homogeneous space with his study of Quantum mechanics.
His study on Random phase approximation is often connected to Quasiparticle as part of broader study in Atomic physics.
His Neutron study necessitates a more in-depth grasp of Nuclear physics.
Jie Meng interconnects Density functional theory, Nuclear matter, Nucleon, Hartree–Fock method and Covariant transformation in the investigation of issues within Quantum electrodynamics.
His most cited work include:
- Relativistic continuum Hartree Bogoliubov theory for ground-state properties of exotic nuclei (566 citations)
- Relativistic continuum Hartree Bogoliubov theory for ground-state properties of exotic nuclei (566 citations)
- Tilted rotation of triaxial nuclei (292 citations)
What are the main themes of his work throughout his whole career to date?
His primary areas of study are Atomic physics, Mean field theory, Neutron, Covariant transformation and Quantum electrodynamics.
His Atomic physics study also includes
- Angular momentum and related Excited state,
- Spectral line which connect with Condensed matter physics.
His Mean field theory study combines topics from a wide range of disciplines, such as Binding energy, Adiabatic process, Wave function and Ground state.
His Neutron research is under the purview of Nuclear physics.
His Covariant transformation study deals with Density functional theory intersecting with Rotation.
His Quantum mechanics study integrates concerns from other disciplines, such as Nuclear theory and Homogeneous space.
He most often published in these fields:
- Atomic physics (59.34%)
- Mean field theory (44.99%)
- Neutron (50.00%)
What were the highlights of his more recent work (between 2016-2021)?
- Neutron (50.00%)
- Covariant transformation (56.72%)
- Density functional theory (45.33%)
In recent papers he was focusing on the following fields of study:
Jie Meng mainly focuses on Neutron, Covariant transformation, Density functional theory, Nuclear theory and Quantum electrodynamics.
Neutron is a subfield of Nuclear physics that Jie Meng studies.
His research in Nuclear physics intersects with topics in Bound state and Hartree.
His Covariant transformation research is multidisciplinary, incorporating elements of Particle physics, Nucleon, Theoretical physics and Nuclear structure.
His Density functional theory study is focused on Quantum mechanics in general.
His Nuclear drip line research integrates issues from Mean field theory and Binding energy.
Between 2016 and 2021, his most popular works were:
- The limits of the nuclear landscape explored by the relativistic continuum Hartree–Bogoliubov theory (54 citations)
- The limits of the nuclear landscape explored by the relativistic continuum Hartree–Bogoliubov theory (54 citations)
- The limits of the nuclear landscape explored by the relativistic continuum Hartree–Bogoliubov theory (54 citations)
In his most recent research, the most cited papers focused on:
- Quantum mechanics
- Quantum field theory
- Neutron
Jie Meng focuses on Neutron, Nuclear theory, Covariant transformation, Nuclear matter and Density functional theory.
His study in Neutron is interdisciplinary in nature, drawing from both Ab initio quantum chemistry methods and Atomic physics.
His Nuclear theory study incorporates themes from Hartree–Fock method, Quantum electrodynamics and Molecular physics.
The Covariant transformation study combines topics in areas such as Nuclear structure and Spin-½.
The subject of his Density functional theory research is within the realm of Quantum mechanics.
His studies in Nuclear physics integrate themes in fields like Bound state and Mean field theory.
This overview was generated by a machine learning system which analysed the scientist’s
body of work. If you have any feedback, you can
contact us
here.
