physics.comp-ph papers | Gist.Science

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.

A distribution-free lattice Boltzmann method for compartmental reaction-diffusion systems with application to epidemic modelling

This paper introduces a distribution-free, single-step simplified lattice Boltzmann method (SSLBM) for compartmental reaction-diffusion systems that eliminates particle distribution functions to achieve a compact, efficient framework, demonstrating superior accuracy and robustness over standard lattice Boltzmann and finite difference approaches when applied to SEIRD epidemic modeling.

Alessandro De Rosis2026-03-23🔬 physics

Time-delay estimation using the Wigner-Ville distribution

This paper proposes a time-delay estimation method based on the Wigner-Ville Distribution (WVD) that outperforms traditional linear time-frequency approaches like the continuous wavelet transform by achieving higher accuracy and lower uncertainty, particularly for nonstationary signals in energetic frequency bands.

L. de A. Gurgel, J. M. de Araújo, L. D. Machado, P. D. S. de Lima2026-03-23🔬 physics

Micromagnetic Modeling of Surface Acoustic Wave Driven Dynamics: Interplay of Strain, Magnetorotation, and Magnetic Anisotropy

This paper presents a unified micromagnetic study of surface acoustic wave-driven spin wave dynamics in CoFeB films, demonstrating that the orientation of magnetic anisotropy acts as a tunable parameter to optimize resonant coupling, particularly when the wave propagates parallel to the external magnetic field.

Florian Millo, Pauline Rovillain, Massimiliano Marangolo, Daniel Stoeffler2026-03-23🔬 cond-mat.mes-hall

Deep learning-based phase-field modelling of brittle fracture in anisotropic media

This paper introduces a novel variational physics-informed deep learning framework that utilizes higher-order B-spline basis functions within the Deep Ritz Method to accurately model brittle crack propagation in anisotropic media, overcoming previous limitations of isotropic and second-order formulations.

N. Plungė, P. Brommer, R. S. Edwards, E. G. Kakouris2026-03-23🔬 physics

From Polyhedra to Crystals: A Graph-Theoretic Framework for Crystal Structure Generation

This paper introduces a novel graph-theoretic framework that encodes the geometry and topology of space-filling polyhedra into dual periodic graphs to systematically generate crystal structures, offering a more efficient and interpretable alternative to conventional random generation methods for accelerating materials discovery.

Tomoyasu Yokoyama, Kazuhide Ichikawa, Hisashi Naito2026-03-20🔬 cond-mat.mtrl-sci

Stability of Continuous Time Quantum Walks in Complex Networks

This study characterizes the stability of continuous-time quantum walks across diverse network topologies under various decoherence models, revealing that while dense and heterogeneous networks exhibit robustness against certain noise types, they suffer rapid decay under edge-based stochastic processes and face a fundamental trade-off between structural localization and coherence preservation that is critically dependent on the initialization node's centrality.

Adithya L J, Johannes Nokkala, Jyrki Piilo, Chandrakala Meena2026-03-20⚛️ quant-ph

Transfer Learning for Neutrino Scattering: Domain Adaptation with GANs

This paper demonstrates that transfer learning with Generative Adversarial Networks effectively extrapolates physics information from synthetic neutrino-carbon scattering data to related processes like neutrino-argon and antineutrino-carbon interactions, significantly outperforming models trained from scratch and maintaining high accuracy even with limited statistics.

Jose L. Bonilla, Krzysztof M. Graczyk, Artur M. Ankowski, Rwik Dharmapal Banerjee, Beata E. Kowal, Hemant Prasad, Jan T. Sobczyk2026-03-20⚛️ nucl-ex

Multigroup Radiation Diffusion on a Moving Mesh: Implementation in RICH and Application to Tidal Disruption Events

The authors present a new multigroup radiation diffusion module for the RICH moving-mesh code, validate it against analytic benchmarks, and apply it to simulate a tidal disruption event that successfully reproduces an early X-ray flash consistent with observational data.

Itamar Giron, Menahem Krief, Nicholas C. Stone, Elad Steinberg2026-03-20🔭 astro-ph

An HHL-Based Quantum-Classical Solver for the Incompressible Navier-Stokes Equations with Approximate QST

This paper presents a hybrid quantum-classical solver that integrates the Harrow-Hassidim-Lloyd (HHL) algorithm with Chebyshev-based approximate quantum state tomography to efficiently solve the incompressible Navier-Stokes equations, successfully validating the approach through accurate simulations of lid-driven cavity and Taylor-Green vortex flows using IBM's Qiskit framework.

Moshe Inger, Steven Frankel2026-03-20⚛️ quant-ph

Multi-Outcome Circuit Optimization for Enhanced Non-Gaussian State Generation

This paper proposes and demonstrates a multi-outcome optimization strategy for photonic quantum circuits that significantly enhances the success probability of generating non-Gaussian states by leveraging multiple useful measurement patterns and aggregating degenerate outcomes, rather than restricting optimization to a single target outcome.

S. Ismailzadeh, B. Abedi Ravan2026-03-20⚛️ quant-ph

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