Latest Articles for Computational Science

Developing an algorithm for the efficient factorization of the Brauer monoid.

Learn to improve optimization using simpler models combined with high-quality data.

Research highlights how disconnectivity graphs reveal Ising machines' strengths and challenges in optimization.

This article analyzes error correction methods in quantum computing using the Bacon-Shor code.

A new approach improves simulations for quasi-2D particle interactions.

Machine learning aids in predicting crystal structures more efficiently than traditional methods.

This research presents a combined approach for quicker protein movement simulations.

Examining how quantum algorithms can enhance our understanding of neural networks.

Researchers develop new methods for preparing Bethe states in quantum systems.

A new method blends local and nonlocal modeling to improve accuracy and reduce costs.

Exploring new techniques in finite element methods for practical applications.

New methods provide solutions to complex functional differential equations in science.

FastTuckerPlus speeds up sparse tensor analysis using local search and GPU technology.

Improving eigenvalue estimation through new SoftFEM techniques.

Investigating the extreme conditions of quark-gluon plasma through heavy-ion collisions.

Improving accuracy in solving physics-related partial differential equations with adaptive sampling.

Machine learning improves artificial viscosity models for enhanced numerical simulations.

A new method improves solving complex equations in multiple dimensions.

Advancing methods for better modeling of fluid shocks in high-speed flows.

This study shows how neural networks enhance preconditioning for solving sparse linear systems.

A new approach improves the efficiency of estimating stabilization parameters in engineering.

A new method addresses the challenge of high-dimensional equations in physics and engineering.

A new framework improves gradient calculations for interconnected optimization problems.

Analyzing how noise affects topological orders and their potential in quantum technology.

A look at how living organisms process information and respond to their environments.

This study enhances fluid simulations using a generative adversarial network within multigrid methods.

Exploring how ions move and affect cell functions.

Examining snapshot methods for better modeling of complex systems over time.

A deep dive into the structure of twisted simplicial distributions and their relevance to quantum mechanics.

HELP framework improves prediction of essential genes in various biological contexts.

Researchers use GPUs to speed up quantum particle simulations effectively.

Exploring the significance of monotonicity and contractivity in reaction networks.

PINDER provides vital data for studying protein interactions and improving therapeutic methods.

Exploring PETScML's impact on scientific machine learning through second-order methods.

Combining large language models and message passing networks improves molecular property predictions.

Innovative method combines deep learning and advanced numerical techniques for solving conservation laws.

This article discusses the benefits of hybrid high-order methods with geometric multigrid for solving linear systems.

New methods enhance accuracy in electron interactions for chemistry and physics.

Combining scattered and regular nodes improves accuracy in complex problem solving.

New protocols aim to improve magic state production in quantum computers.