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 ...

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

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 ...

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 ...

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

Traditional tabular implementations in reinforcement learning often face limitations in handling large state or action spaces. Enter function approximation, a paradigm that overcomes these constraints ...

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

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 ...

Clinical trials have traditionally followed a fixed design, in which patient allocation to treatments is fixed throughout the trial and specified in the protocol. The primary goal of this static ...

Many banks currently use the loss distribution approach (LDA) for estimating economic and regulatory capital for operational risk under Basel's advanced measurement approach. The LDA requires the ...