A University of Sydney quantum physicist has developed a new approach to quantum error correction that could significantly ...
Abstract: Solving partial differential equations is a key focus of research in scientific computing. Traditional neural operator methods often face challenges in capturing both global features and ...
This repository contains code for the paper: "Enabling Local Neural Operators to perform Equation-Free System-Level Analysis" G. Fabiani, H. Vandecasteele, S. Goswami, C. Siettos, I.G. Kevrekidis ...
Most AI providers try to enhance their products by training them with both public information and user data. However, the latter method puts a privacy-conscious company like Apple in a difficult ...
ABSTRACT: This work focuses on the development and analysis of a financial system using advanced mathematical modeling techniques. Starting from an ordinary financial system, we extend it to a ...
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 ...
Abstract: Neural operators are a class of neural networks to learn mappings between infinite-dimensional function spaces, and recent studies have shown that using neural operators to solve partial ...
Euler Method: The simplest numerical method for solving ODEs, which uses the derivative to project forward. [ y_{n+1} = y_n + h \cdot f(x_n, y_n) ] Heun's Method (Improved Euler Method): A two-step ...
Some results have been hidden because they may be inaccessible to you
Show inaccessible results