What is he best known for?
The fields of study he is best known for:
- Mathematical analysis
- Quantum mechanics
- Partial differential equation
His primary areas of study are Mathematical analysis, Pure mathematics, Attractor, Invariant and Dynamical systems theory.
He studied Mathematical analysis and Invariant measure that intersect with Manifold.
His research integrates issues of Stochastic partial differential equation and Ordinary differential equation in his study of Pure mathematics.
His Stochastic partial differential equation research incorporates elements of Lipschitz continuity, Random dynamical systems and Invariant.
The Attractor study combines topics in areas such as Dissipative system and Differential equation.
Kening Lu focuses mostly in the field of Invariant, narrowing it down to matters related to Banach space and, in some cases, Inertial manifold, Lyapunov exponent and Ergodic theory.
His most cited work include:
- Random attractors for stochastic reaction–diffusion equations on unbounded domains (245 citations)
- Invariant manifolds for stochastic partial differential equations (213 citations)
- ATTRACTORS FOR STOCHASTIC LATTICE DYNAMICAL SYSTEMS (206 citations)
What are the main themes of his work throughout his whole career to date?
Kening Lu spends much of his time researching Mathematical analysis, Pure mathematics, Attractor, Invariant and Stochastic partial differential equation.
In his papers, Kening Lu integrates diverse fields, such as Mathematical analysis and Multiplicative noise.
Kening Lu has researched Pure mathematics in several fields, including Random dynamical systems and Invariant.
The various areas that Kening Lu examines in his Attractor study include Dynamical systems theory, Compact space, Klein–Gordon equation and Schrödinger equation.
His biological study spans a wide range of topics, including Fractional calculus, Applied mathematics and Ordinary differential equation.
He combines subjects such as Partial differential equation and Lipschitz continuity with his study of Nonlinear system.
He most often published in these fields:
- Mathematical analysis (80.85%)
- Pure mathematics (30.85%)
- Attractor (35.11%)
What were the highlights of his more recent work (between 2016-2021)?
- Mathematical analysis (80.85%)
- Attractor (35.11%)
- Pure mathematics (30.85%)
In recent papers he was focusing on the following fields of study:
Kening Lu focuses on Mathematical analysis, Attractor, Pure mathematics, Stationary process and Multiplicative noise.
His research on Mathematical analysis focuses in particular on Stochastic differential equation.
His Attractor research is multidisciplinary, relying on both Uniqueness and Pullback.
In his study, which falls under the umbrella issue of Pure mathematics, Phase is strongly linked to Class.
The Stationary process study which covers Approximations of π that intersects with Stochastic evolution and Almost surely.
His Banach space study also includes fields such as
- Measure that connect with fields like Dynamical systems theory,
- Nonlinear system, which have a strong connection to Differential equation.
Between 2016 and 2021, his most popular works were:
- Wong-Zakai approximations and attractors for stochastic reaction-diffusion equations on unbounded domains (35 citations)
- Limiting behavior of dynamics for stochastic reaction-diffusion equations with additive noise on thin domains (24 citations)
- Wong–Zakai Approximations and Long Term Behavior of Stochastic Partial Differential Equations (22 citations)
In his most recent research, the most cited papers focused on:
- Mathematical analysis
- Quantum mechanics
- Partial differential equation
His primary scientific interests are in Attractor, Uniqueness, Pullback attractor, Convergence and Multiplicative noise.
Kening Lu frequently studies issues relating to Pullback and Attractor.
His Pullback research entails a greater understanding of Pure mathematics.
His Pullback attractor study incorporates themes from Zero and Reaction–diffusion system.
His study of Convergence brings together topics like Applied mathematics, Stochastic partial differential equation, Navier–Stokes equations, Lipschitz continuity and Term.
His Multiplicative noise research incorporates elements of Stationary process, Approximations of π and Mathematical analysis.
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.
