🔬 Category

physics.comp-ph

903 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.

Challenges in predicting positron annihilation lifetimes in lead halide perovskites: correlation functionals and polymorphism

This study demonstrates that the choice of electron-positron correlation functional, particularly the use of the non-local weighted density approximation (WDA), is critical for accurately predicting positron annihilation lifetimes in lead halide perovskites, revealing that previous discrepancies in theoretical predictions and experimental interpretations of cation vacancies stem from the sensitivity of these materials to the specific approximation used.

Kajal Madaan, Guido Roma, Jasurbek Gulomov, Pascal Pochet, Catherine Corbel, Ilja Makkonen2026-04-23🔬 cond-mat.mtrl-sci

Machine learning moment closure models for the radiative transfer equation IV: enforcing symmetrizable hyperbolicity in two dimensions

This paper extends a machine learning moment closure framework for the radiative transfer equation from one to two dimensions by leveraging the block-tridiagonal structure of the classical PNP_N model to derive explicit algebraic conditions that guarantee symmetrizable hyperbolicity through a learnable, symmetric positive definite parametrization.

Juntao Huang2026-04-23🔬 physics

Domain-Wall-Mediated Ultralow-Barrier Sliding and Pinning in Ferroelectric Moiré Superlattices Revealed by Machine Learning

This study employs machine-learning molecular dynamics to reveal that thermally driven interlayer sliding in ferroelectric MoS₂ moiré superlattices occurs via a domain-wall-mediated, ultralow-barrier collective reconstruction pathway rather than rigid translation, and that minimal sulfur vacancies can trigger a transition from long-range sliding to localized pinning.

Jia-Wen Li, Sheng Meng, Xinghua Shi, Jin Zhang, Wei-Hai Fang2026-04-23🔬 cond-mat.mtrl-sci

A Cartesian grid-based boundary integral method for moving interface problems

This paper presents a stable and efficient Cartesian grid-based boundary integral method that reformulates elliptic and parabolic PDEs into boundary integral equations solved via matrix-free GMRES and finite difference-based integral evaluation, utilizing θ−L\theta-L variables to simplify mesh preservation and enable robust time-stepping for complex moving interface problems like Hele-Shaw flow and Stefan solidification.

Han Zhou, Shuwang Li, Wenjun Ying2026-04-22🔬 physics

Adaptive hyperviscosity stabilisation for the RBF-FD method in solving advection-dominated transport equations

This paper introduces a general, adaptive hyperviscosity stabilization procedure for the RBF-FD method that dynamically determines the viscosity constant based on the spectral radius of the evolution matrix to efficiently and stably solve advection-dominated transport equations on unbounded domains without empirical tuning.

Miha Rot, Žiga Vaupotič, Andrej Kolar-Požun, Gregor Kosec2026-04-22🔬 physics

Diffusion Synthetic Acceleration for polytopic discretisations of Boltzmann transport

This paper presents a computational study demonstrating that a modified interior penalty (MIP) formulation for Diffusion Synthetic Acceleration (DSA) applied to polytopic discontinuous Galerkin discretizations of SNS_N transport equations maintains robust convergence across various optical and scattering regimes, outperforming the classical symmetric interior penalty (SIP) approach which can lose stability in intermediate conditions.

Ansar Calloo, Matthew Evans, François Madiot, Tristan Pryer2026-04-22🔢 math

Nonuniform Iterative Phasing Framework and Sampling Requirements for 3D Dynamical Inversion from Coherent Surface Scattering Imaging

This paper introduces a nonuniform iterative phasing framework that combines iterative-projection techniques with fast nonuniform Fourier inversion to efficiently reconstruct high-resolution 3D structures from coherent surface scattering imaging data, effectively addressing challenges posed by dynamical scattering, nonuniform sampling, and phase retrieval while validating the approach on simulated nanostructures.

Jeffrey J. Donatelli, Miaoqi Chu, Zixi Hu, Zhang Jiang, Nicholas Schwarz, Jin Wang, James A. Sethian2026-04-22🔬 physics

Neural Operator Representation of Granular Micromechanics-based Failure Envelope

This paper proposes a differentiable, physics-informed neural operator that efficiently learns the mapping between microstructural configurations and macroscopic failure envelopes for granular materials, enabling rapid forward prediction and inverse identification while ensuring mechanical admissibility through convexity constraints and reducing computational costs via active learning.

Jinkyo Han, Payam Poorsolhjouy, Bahador Bahmani2026-04-22🔬 physics