Latest Articles for Computational Science

Research sheds light on nanodiscs' structure and stability with apoE3.

A look into graph theory's algebraic connectivity, focusing on trees and their structures.

This article examines the link between deep learning and renormalization group methods using the Ising model.

This paper investigates the properties of planar graph powers and their implications.

A method to improve graph transformers efficiently and with less resource use.

Exploring how massive objects manipulate light and reveal dark matter.

Using GPUs to speed up reservoir simulations can improve efficiency and reduce computation times.

A look at randomized methods for approximating Tukey's depth in complex data.

New computational methods improve mRNA design for vaccines and therapies.

MULAN integrates sequence and structural data to enhance understanding of proteins.

Machine learning transforms event reconstruction in particle physics, improving accuracy and efficiency.

How quantum networks are transforming secure communication and computing.

LPNets enhance the simulation of Hamiltonian systems, preserving essential properties.

A new model improves the accuracy of simulations in biological systems.

Studying mixed quantum-semiclassical systems enhances insights into complex physical phenomena.

Research reveals how mutations impact protein behavior, aiding future medical applications.

A look at using the Hybrid High-Order method for biharmonic equations.

A look at the lattice-symmetries package for quantum many-body physics.

Revolutionize how we visualize high-dimensional data using prior knowledge.

Graph-JEPA offers a new method for analyzing complex graph data effectively.

Hamiltonian learning advances quantum systems understanding, focusing on steady states and degeneracy.

PolyGET merges speed and accuracy in polymer simulations, enhancing scientific research.

Research reveals connections between non-stabilizerness and entanglement in quantum systems.

New techniques significantly improve efficiency and performance of quantum memory using barium atomic vapor.

Learn about ROMC and its application in likelihood-free inference.

Machine learning enhances sampling in lattice quantum chromodynamics for better predictions.

A look into improved clustering methods using multi-swap local search.

Exploring the role of response functions in quantum physics and their practical applications.

Research reveals new connections between lattice geometry and the Ising model's critical behavior.

New techniques in CORSIKA 8 enhance radio signal analysis for high-energy particles.

A new method enhances processing for sparse tensor networks, improving performance.

Combining JAX and Firedrake enhances numerical methods for solving partial differential equations.

Combining machine learning and OFDFT enhances molecular analysis efficiency and accuracy.

New methods enhance simulations of multiscale systems with improved accuracy and stability.

New basis sets improve calculations for heavy elements, enhancing material property predictions.

A new co-scheduling algorithm enhances resource management in high-performance computing systems.

Exploring the implications of exponential potentials in quantum systems.

A novel approach for addressing elliptic PDEs with irregular boundaries.

New methods improve sampling from complex distributions for accurate estimations.

Leveraging Grassmann tensor networks to simplify complex calculations in theoretical physics.