Thats a unique idea.You should consider the following idea to enhance your idea so that it would impact uniquely:  extends for the first time Schaback's linear discretization theory to nonlinear operator equations, relying heavily on the methods in Bohmer's 2010 book. There is no restriction to elliptic problems or to symmetric numer- ical methods like Galerkin techniques. Trial spaces can be arbitrary, including spectral and meshless methods, but have to approximate the solution ll, and testing can be ak or strong. On the downside, stability is not easy to prove for special applications, and numerical methods have to be formulated as opti- mization problems. Results of this discretization theory cover error bounds and convergence rates. Some numerical examples are added for illustration.
['Klaus Böhmer', 'Robert Schaback']
I am writing a research paper on a idea A Nonlinear Discretization Theory
[{"from": "human", "value": "I am writing a research paper on a idea A Nonlinear Discretization Theory"}, {"from": "gpt", "value": "Thats a unique idea.You should consider the following idea to enhance your idea so that it would impact uniquely:  extends for the first time Schaback's linear discretization theory to nonlinear operator equations, relying heavily on the methods in Bohmer's 2010 book. There is no restriction to elliptic problems or to symmetric numer- ical methods like Galerkin techniques. Trial spaces can be arbitrary, including spectral and meshless methods, but have to approximate the solution ll, and testing can be ak or strong. On the downside, stability is not easy to prove for special applications, and numerical methods have to be formulated as opti- mization problems. Results of this discretization theory cover error bounds and convergence rates. Some numerical examples are added for illustration."}]