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SOLUTIONS FOR SUPERLINEAR(n-1,1)CONJUGATE BOUNDARY VALUE PROBLEMS
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作者 娄本东 《Acta Mathematica Scientia》 SCIE CSCD 2001年第2期259-264,共6页
The author investigates the existence of positive and nontrivial solutions for superlinear (n - 1, 1) conjugate boundary value problems by means of topological degree theory and cone theory. The main theorems improve ... The author investigates the existence of positive and nontrivial solutions for superlinear (n - 1, 1) conjugate boundary value problems by means of topological degree theory and cone theory. The main theorems improve some results published recently. 展开更多
关键词 Boundary value problems topological degree CONE positive solutions non- trivial solutions
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ON THE STABILITY OF FORCED DISSIPATIVE NONLINEAR SYSTEM
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作者 陈达段 刘晓明 施惟慧 《Applied Mathematics and Mechanics(English Edition)》 SCIE EI 1996年第6期541-548,共8页
This paper, applying the stratification theory, proves the instability of certain initial (boundary) Value problem of forced dissipative nonlinear system in atmospheric dynamies. An example in given.
关键词 construction of solution space. stralificalion. trivial layer
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A new method to solve the Reynolds equation including mass-conserving cavitation by physics informed neural networks(PINNs)with both soft and hard constraints
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作者 Yinhu XI Jinhui DENG Yiling LI 《Friction》 SCIE EI CAS CSCD 2024年第6期1165-1175,共11页
In this work,a new method to solve the Reynolds equation including mass-conserving cavitation by using the physics informed neural networks(PINNs)is proposed.The complementarity relationship between the pressure and t... In this work,a new method to solve the Reynolds equation including mass-conserving cavitation by using the physics informed neural networks(PINNs)is proposed.The complementarity relationship between the pressure and the void fraction is used.There are several difficulties in problem solving,and the solutions are provided.Firstly,the difficulty for considering the pressure inequality constraint by PINNs is solved by transferring it into one equality constraint without introducing error.While the void fraction inequality constraint is considered by using the hard constraint with the max-min function.Secondly,to avoid the fluctuation of the boundary value problems,the hard constraint method is also utilized to apply the boundary pressure values and the corresponding functions are provided.Lastly,for avoiding the trivial solution the limitation for the mean value of the void fraction is applied.The results are validated against existing data,and both the incompressible and compressible lubricant are considered.Good agreement can be found for both the domain and domain boundaries. 展开更多
关键词 Reynolds equation mass-conserving cavitation physics informed neural networks hard constraints trivial solution
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