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.

The universal growth of magnetic energy during the nonlinear phase of subsonic and supersonic small-scale dynamos

By analyzing a large ensemble of simulations across subsonic to supersonic regimes, this study reveals that while the nonlinear growth rate of small-scale dynamos varies from linear to quadratic depending on flow compressibility, the process consistently converts a fixed fraction of turbulent kinetic energy into magnetic energy over a characteristic duration of approximately 20 outer-scale turnover times.

Neco Kriel, James R. Beattie, Mark R. Krumholz, Jennifer Schober, Patrick J. Armstrong2026-05-01🔬 physics

Predicting parameters of a model cuprate superconductor using machine learning

This study demonstrates that a U-Net deep learning architecture can effectively solve the inverse problem of predicting cuprate superconductor Hamiltonian parameters from phase diagrams, achieving high accuracy and revealing physically interpretable patterns of parametric sensitivity.

V. A. Ulitko, D. N. Yasinskaya, S. A. Bezzubin, A. A. Koshelev, Y. D. Panov2026-05-01🔬 cond-mat

Rigorous electromagnetic quasinormal-mode method made easy for users

This paper introduces a simplified, accessible, and ultrafast method for computing electromagnetic quasinormal modes by combining numerical techniques with accurate approximations, implemented in an open-source package within commercial photonics software to bridge the gap between advanced QNM theory and standard real-frequency simulation practices.

Tong Wu, Philippe Lalanne2026-05-01🔬 physics.optics

Towards single-shot coherent imaging via overlap-free ptychography

This paper presents an extended PtychoPINN framework that enables overlap-free, single-shot coherent diffraction imaging and accelerates conventional multi-shot ptychography by coupling a differentiable forward model with a Poisson likelihood, achieving high-fidelity reconstructions with significantly reduced data requirements and increased throughput on experimental synchrotron and XFEL datasets.

Oliver Hoidn, Albert Vong, Aashwin Mishra, Steven Henke, Matthew Seaberg2026-05-01🔬 physics.optics

Experimentally Accurate Graph Neural Network Predictions of Core-Electron Binding Energies

This paper presents an experimentally accurate, interpretable graph neural network model called AugerNet that predicts carbon 1s core-electron binding energies in organic molecules with a mean absolute error of 0.33 eV by leveraging chemically informed node features and E(3)-equivariance to capture local bond environments and generalize to larger systems.

Adam E. A. Fouda, Joshua Zhou, Rodrigo Ferreira, Patrick Phillips, Valay Agarawal, Bhavnesh Jangid, Jacob J. Wardzala, Rui Ding, Junhong Chen, Nicole Tebaldi, Phay J. Ho, Laura Gagliardi, Linda Young2026-05-01🔬 physics

Computation of frequency- and time-domain Jacobians in optical tomography with Monte Carlo simulations

This paper presents a complete theoretical framework and open-source Monte Carlo implementation for computing frequency- and time-domain Jacobians in optical tomography, demonstrating their necessity for accurate modeling in low-scattering regimes and the benefits of realistic detector modeling for short source-detector separations.

Pauliina Hirvi, Jaakko Olkkonen, Qianqian Fang, Ilkka Nissilä2026-05-01🔬 physics.optics

VibroML: an automated toolkit for high-throughput vibrational analysis and dynamic instability remediation of crystalline materials using machine-learned potentials

VibroML is an open-source Python toolkit that leverages machine-learned potentials and genetic algorithms to automate the remediation of dynamical instabilities, validate finite-temperature stability, and systematically explore compositional spaces, thereby transforming high-throughput materials screening from mere stability verification into a comprehensive workflow for generating physically viable crystalline structures.

Rogério Almeida Gouvêa, Gian-Marco Rignanese2026-05-01🔬 cond-mat.mtrl-sci

Compressibility of micromagnetic solutions in tensor train format

This paper demonstrates that representing 3D micromagnetic states in tensor train format overcomes the cubic scaling limitations of traditional grid-based methods by exploiting spatial sparsity, achieving a significantly more efficient parameter count that scales as $L^{1.8}$ and $(1/a)^{1.2}$ for flux-closure configurations.

Thierry Valet, Nicolas Vukadinovic2026-05-01🔬 cond-mat.mes-hall

Parallel athermal quasistatic deformation stepping of molecular systems

This paper introduces a novel parallel athermal quasistatic deformation scheme that utilizes a two-level stepping approach to significantly accelerate the computation of molecular system trajectories, achieving speed-ups between 2.02 and 6.33 times while maintaining simulation accuracy.

Maximilian Reihn, Franz Bamer, Benjamin Stamm2026-04-30🔢 math-ph

Time-Dilation Methods for Extreme Multiscale Timestepping Problems

This paper introduces a generalized time-dilation framework that modulates evolution via a continuous space-time factor to overcome extreme multiscale timestep limitations in astrophysical simulations, enabling speedup factors exceeding $10^4$ while preserving correct local steady states and avoiding arbitrary scale separations.

Philip F. Hopkins, Elias R. Most2026-04-30🔭 astro-ph

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