Characterizing the spiking statistics of neurons receiving noisy synaptic input is a central problem in computational neuroscience. Monte Carlo approaches to this problem are computationally expensive ...

Approximation methods in function spaces characterise how well complex functions can be represented or recovered using limited information such as function values or ...

Approximation theory investigates how complex functions or data can be represented by simpler mathematical entities—such as polynomials, splines, wavelets or ...

Analytical methods deals with using the theorems and formulaes which are more accurate. Usually these are applicable for simple problems only. Certain conditions like pre-stress, defects, rate of ...

Abstract: This paper deals with approximation methods for fractional operators. Fractional calculus is a new concept for engineers and we need tools to analyze and utilize the same. One way to do the ...

Adequate mathematical modeling is the key to success for many real-world projects in engineering, medicine, and other applied areas. As soon as an appropriate mathematical model is developed, it can ...

Abstract: As computing devices continue growing explosively, computational efficiency is increasingly important. To improve the efficiency of computations, approximate computing is widely used in ...

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Have you ever wondered how we approximate the area under a curve when finding exact solutions isn't practical? Numerical methods provide powerful tools to estimate definite integrals, and today, I’m ...

Covers asymptotic evaluation of integrals (stationary phase and steepest descent), perturbation methods (regular and singular methods, and inner and outer expansions), multiple scale methods, and ...