Adequate mathematical modeling is the key to success for many real-world projects in engineering, medicine, and other applied areas. Once a well-suited model is established, it can be thoroughly ...

Approximation theory and asymptotic methods form a foundational framework that bridges classical ideas with modern numerical analysis, enabling researchers to obtain practical, near‐optimal solutions ...

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

Applied and Computational Mathematics and Statistics, University of Notre Dame, Notre Dame, IN, USA Characterizing the spiking statistics of neurons receiving noisy synaptic input is a central problem ...

Abstract: In this letter, we propose HV-Net, a new method for hypervolume approximation in evolutionary multiobjective optimization. The basic idea of HV-Net is to use DeepSets, a deep neural network ...

A wealth of computationally efficient approximation methods for Gaussian process regression have been recently proposed. We give a unifying overview of sparse approximations, following ...

This course teaches commonly used approximation methods in quantum mechanics. They include time-independent perturbation theory, time-dependent perturbation theory, tight binding method, variational ...