922 papers
Computational physics bridges the gap between abstract theory and real-world observation by using powerful computers to solve complex physical problems. This field allows scientists to simulate everything from the collision of subatomic particles to the swirling dynamics of galaxies, offering insights that traditional experiments alone cannot provide.
On Gist.Science, we continuously process every new preprint in this category from arXiv to make these breakthroughs accessible to everyone. Each entry is accompanied by both a clear, plain-language explanation and a detailed technical summary, ensuring that researchers and curious readers alike can grasp the significance of the latest findings without getting lost in dense equations.
Below are the latest papers in computational physics, curated to keep you at the forefront of this rapidly evolving discipline.
The paper proposes the Pseudo Physics-Informed Neural Operator (PPI-NO) framework, which enhances data-scarce operator learning by iteratively coupling neural operators with a surrogate physics system derived from rudimentary principles, thereby significantly improving predictive accuracy without requiring ground-truth physical laws.
This paper introduces XtalOpt Version 14, an enhanced evolutionary multi-objective algorithm that utilizes Pareto optimization and integrates various computational potentials to efficiently predict ground-state crystal structures with variable compositions for functional materials.
This paper introduces a Fully Convolutional Neural Network (FCNN) based non-local closure for the electron pressure tensor in turbulent magnetosheath simulations, demonstrating that it significantly outperforms local closures in reconstructing energy channels and pressure-strain interactions while showing favorable scaling with increased training data.
This paper presents the successful porting of the TRIMEG gyrokinetic plasma simulation code to GPU architectures using the portable OpenMP API, demonstrating significant speedups and verified physical accuracy through hybrid MPI-OpenMP parallelization and Ion Temperature Gradient mode simulations.
This paper proposes a new implicit numerical method in Euler variables to solve one-dimensional cold plasma equations with density-dependent collision coefficients, effectively overcoming computational challenges associated with the loss of hyperbolicity while confirming theoretical predictions regarding solution smoothness.
This paper introduces a tensor network-based framework for constructing "tensorized" orbitals that overcome the computational constraints of traditional basis sets, enabling more accurate and compact representations that significantly reduce energy errors in quantum chemistry calculations.
This paper introduces MITONet, a novel neural operator emulator that achieves real-time, high-accuracy forecasting of complex coastal and riverine shallow water dynamics with significant computational speedups (100x–1,250x) and robust generalization to unseen conditions and parameters.
This paper introduces a thermodynamically consistent Adsorption Layer framework that couples interfacial friction, viscous stresses, and adsorption dynamics to explain slip length as an emergent, geometry-dependent property, successfully resolving discrepancies in classical models regarding water slippage in carbon nanotubes and flow near moving contact lines.
This paper presents two novel, highly accurate, and scalable spectral Poisson solvers implemented in the NIRVANA-III code that efficiently handle vertical vacuum boundary conditions within the shearing-box framework, thereby enabling high-resolution local studies of self-gravitating astrophysical fluids.
This paper provides a comprehensive review of variational quantum computing for quantum simulation, detailing its foundational principles, hybrid quantum-classical implementations, and critical challenges such as trainability and noise within the NISQ era, while emphasizing the distinct role of quantum data in advancing the field.