1259 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.
This paper proposes a Real-Time Physics-Driven Fourier-Spectral solver that leverages untrained neural networks with spectral-domain dimensionality reduction and specialized operators to achieve sub-second, high-fidelity reconstruction of highly nonlinear inverse scattering problems, offering a 100-fold speedup over existing methods.
This paper demonstrates the effectiveness of a Physics-Informed Neural Network with three-stage loss optimization as a mesh-free solver for one-dimensional coupled electro-elastodynamic wave propagation, achieving low global relative L2 errors for displacement and electric potential while highlighting remaining challenges in error accumulation and system stiffness.
This paper presents an anisotropic goal-oriented error estimator based on the Dual Weighted Residual method for time-dependent convection-dominated problems, which utilizes discontinuous space-time elements and directional error indicators to drive efficient anisotropic hp-adaptive refinements that outperform isotropic methods in capturing sharp layers.
This paper introduces an end-to-end generative framework that integrates Large Language Models with differentiable ray-tracing to autonomously translate semantic requirements into valid, high-performance diffraction-limited optical designs across diverse applications, thereby democratizing optical engineering.
Using 3D molecular dynamics simulations, this study demonstrates that geometric funnel asymmetry in nanofluidic cascades can drive a "reverse" rectification effect in the ballistic regime, causing significant particle accumulation on the narrow side without external pumps, thereby challenging standard entropic transport theories.
This study reveals that phasons, unique degrees of freedom in quasiperiodic order, drive diverse nucleation pathways in icosahedral quasicrystals by enabling temperature-dependent transitions between direct and symmetry-detour mechanisms, thereby resolving the paradox of how distinct real-space symmetries can yield thermodynamically degenerate bulk structures with identical diffraction patterns.
This paper introduces M-CODE, an open-source, ontology-based categorization system that classifies materials structures by dimensionality, complexity, and evolutionary provenance to support standardized data management and reproducible dataset generation in AI-driven materials science.
This study demonstrates that combining density functional theory with physically informed descriptors enables accurate and chemically transferable machine learning predictions of dopant formation energies in TiO monolayers, even when trained on small, well-curated datasets.
This paper demonstrates that while closed quantum systems can be defined by local boundary conditions, open systems cannot due to the uncertainty principle, and proposes a method using a small numerical lattice to simulate open systems while preserving the physical reality of infinitely extended waves.
This paper addresses the limitations of conventional local envelope-function theory in modeling valley splitting for modern Si/SiGe nanostructures by formulating an exact non-local multi-valley model that ensures energy-reference invariance and demonstrating that a simple spectrally filtered local approximation can effectively restore this crucial physical property.