Latest Articles for Computational Science

G2C3 improves simulations of plasma microturbulence in fusion devices using neural networks.

Explore the structure and applications of 2-quasi best match graphs in biology.

Combining DEER data with AlphaFold2 enhances protein shape predictions.

Combining Parareal and neural networks offers a quicker way to solve ODEs.

Examining how averaged dynamics and randomness improve learning models.

Discover the importance and flexibility of intrinsically disordered proteins in biology.

Researchers use lattice QCD to uncover nucleon properties and their fundamental interactions.

A new method to simplify simulations in agent-based models by combining similar agents.

Neural networks enhance solutions for complex partial differential equations.

Examining turbulence with a focus on Ekman-Navier-Stokes equations and numerical simulations.

A novel approach using Galerkin-Boltzmann and neural networks to analyze fluid behavior.

New quantum method improves molecular energy calculations significantly.

Techniques like manifold learning help scientists interpret large datasets effectively.

Advanced Python code facilitates the study of transversity PDFs in particle physics.

A study on ionisation in hydrogen using classical molecular dynamics simulations.

Combining domain decomposition and operator inference for faster simulations.

TomoPicker simplifies finding particles in Cryo-ET images, reducing manual work significantly.

Research examines complex quantum states in lattice gauge theories using the Schwinger model.

Exploring the fusion of machine learning and molecular mechanics for better simulations.

Researchers use machine learning to predict information spread in quantum systems.

Introducing JuliaQCD, software for simulating quark and gluon interactions in QCD.

Streamlining digital twins through reduced order models and high-performance computing.

This article discusses the vibrational states of methanol and their significance.

Learn how specialized basis sets enhance molecular property predictions.

Explores the impact of PQAE and QAE on quantum data processing.

BaNDyT combines simulations and networks to advance protein research.

New methods combine neural networks and finite element techniques to improve fluid flow simulations.

Combining traditional methods with AI to enhance PDE solutions.

A new method combines HPC, quantum computing, and AI for chemistry.

A look at how mixed FEM aids in studying poroelastic materials.

Exploring how light interacts with matter affects science and technology.

Optical computing offers efficient methods for solving complex partial differential equations.

Evolvoid uses genetic algorithms to model cellular shapes inspired by living tissues.

Innovative approaches improve the study of excited states in quantum systems.

Exploring how quantum principles reshape our view of population changes over time.

New technique enhances network analysis using approximate equitable partitions.

An overview of the implicit Euler method and its applications in differential inversion.

A new method improves neural network efficiency in scientific applications.

A look into how numerical relativity shapes our understanding of gravitational waves.

Methods to enhance efficiency in simulating complex turbulent flows.