Latest Articles for Computational Science

New methods enhance predictions of protein folding and behavior using machine learning.

Streamline complex problem simulations using reduced order models and effective management strategies.

Exploring the hybridized discontinuous Galerkin method for fluid dynamics.

A novel approach using reinforcement learning improves quantum state diagonalization efficiency.

SpComp enhances efficiency in sparse matrix calculations through smart optimization.

WFL package streamlines atomistic simulations and machine learning interatomic potentials.

New methods improve the speed and accuracy of compressible flow simulations.

Study how quantum walks reveal complex particle behavior in quantum systems.

A novel approach enhances the generation of molecular conformers for drug discovery.

Mol-GDL enhances molecular predictions by integrating covalent and non-covalent interactions.

Improving plasma predictions through advanced computational methods for nuclear fusion.

Exploring the role of homotopy methods in coupled-cluster theory for better quantum chemistry solutions.

Researchers utilize machine learning to improve particle simulations at CERN's Large Hadron Collider.

A new projection method improves accuracy in simulating fluid flows.

This study examines conditions for embedding k-factors in k-partite hypergraphs.

This study focuses on calculating PDFs to understand nucleon structure.

This article explores a framework for managing noise in quantum computers through partial error correction.

A look at transport equations and their significance across various fields.

Exploring methods and applications of stochastic partial differential equations in various fields.

A new algorithm improves the speed of solving Modified Poisson-Boltzmann equations for charged systems.

PINNsFormer enhances accuracy in solving partial differential equations using advanced Transformer structures.

New tools enhance the analysis of crystal structures and their properties.

Exploring fixed-point tensors and their role in critical systems.

New adaptive methods improve Bayesian inference with Hamiltonian Monte Carlo.

Exploring the Hyena Neural Operator's efficiency in solving partial differential equations.

LHCb upgrades aim to improve data processing for particle research.

PPIscreenML improves accuracy in identifying protein interactions using advanced machine learning techniques.

A new framework improves predictions for complex hyperbolic PDEs with discontinuities.

Researchers unveil a model to study quantum spin liquids and their environmental interactions.

Machine learning enhances density functional theory for inhomogeneous fluids.

Examining the stability of waves in mathematical equations over time.

Effective predictions from limited simulations for complex jet-explosive interactions.

A new method for measuring quantum entanglement using SVD entanglement entropy reveals deeper connections.

GACN combines GCL and GAN to improve graph representation learning.

Researchers enhance data filtering in Belle II experiments using neural networks to identify relevant events.

Exploring how to arrange quantum states for better information extraction.

New method simplifies simulations for complex physical problems.

A guide to the Raviart-Thomas method for solving boundary value problems.

Exploring Bayesian estimation for precise temperature measurement in quantum thermometry.

Understanding decoherence is vital for advancing quantum computing technologies.