Latest Articles for Computational Science

New methods boost efficiency in analyzing experimental data in astroparticle physics.

A specialized machine learning model improves particle data analysis efficiency and accuracy.

Research reveals key details about exotic hadrons and their interactions.

New methods improve RNA structure prediction and engineering through advanced machine learning techniques.

A new framework for efficient quantum error correction through dynamic coding.

A look into the thermodynamics and properties of quantum gases.

Research explores measurement effects on quantum systems and error mitigation strategies.

This new model improves diffusion predictions and analysis through energy dynamics.

A new method uses deep learning to improve quantum many-body energy calculations.

Nash states offer new insights into quantum systems using concepts from game theory.

New methods improve understanding of molecular interactions and behaviors.

A new method predicts optimal mesh spacing for fluid simulations, enhancing efficiency.

A new algorithm improves sampling in molecular dynamics using normalizing flows and collective variables.

This study introduces tensor-train networks to enhance flow simulation accuracy and efficiency.

A new method improves accuracy in singularly perturbed reaction-diffusion problems.

This article discusses four-qudit codes for effective error correction in quantum computing.

A new loss function, Astral, boosts the performance of physics-informed neural networks.

Examining fluid behavior with a numerical method for enhanced understanding.

Research shows conditioning methods improve drug-like molecule generation.

A new method improves wave propagation efficiency.

A new algorithm improves eigenstate estimation in quantum systems.

A new method improves analysis of complex datasets using advanced techniques.

Discover the role of operator learning in advancing data-driven predictions across various fields.

Exploring the dynamics of neutron stars through advanced simulations and observations.

These models assist scientists in generating complex 3D molecular structures for drug development.

A new approach enhances quantum computing scalability with modular designs.

qFit-ligand improves understanding of ligand shapes and protein interactions.

Exploring new methods for more effective quantum system simulations.

Recent findings reveal the presence and significance of quantum magic in top quark experiments.

SWIRL simplifies complex scientific workflows for better efficiency and interoperability.

Research improves simulations of binary systems, enhancing understanding of gravitational waves.

Improving quantum methods to solve the Poisson equation in fluid dynamics.

Two new methods for efficient solutions in equations and inclusions using variance reduction.

Exploring entanglement entropy and its significance in quantum mechanics.

NGOs utilize neural networks to simplify solving complex partial differential equations efficiently.

A new metric offers insights into quantum circuit learnability without extensive training.

GFN-ROM enhances model order reduction using graph neural networks for efficient simulations.

Researchers improve glueball identification using a two-level algorithm in lattice QCD.

NeuralSCF combines machine learning with DFT for efficient electronic structure analysis.

A new method enhances training efficiency for complex equations using variable scaling.