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.

On the Intrinsic Link between Gradient Strengthening and Passivation Onset in Single Crystal Plasticity

This paper develops a thermodynamically consistent finite-deformation gradient crystal plasticity framework to demonstrate that constitutive laws responsible for size-dependent yield strengthening inherently produce a pronounced, nearly elastic response under passivation boundary conditions, thereby establishing a fundamental link between gradient-induced strengthening and boundary-driven mechanical elevation driven by dissipative effects.

Habib Pouriayevali2026-03-03🔬 cond-mat.mtrl-sci

Towards an understanding of magnesium in a biological environment: A density functional theory study

Using density functional theory, this study investigates the interactions between magnesium surfaces, magnesium hydroxide layers, and specific amino acids to reveal that the hydroxide layer binds weakly to the metal surface and that bulk formation becomes energetically favorable after only a few layers, offering insights into the early-stage corrosion behavior of biodegradable magnesium implants.

Miranda Naurin, Sally Aldhaim, Moltas Elliver, Ludwig Hagby, J. Didrik Nilsson, Elsebeth Schröder2026-03-03🔬 cond-mat.mtrl-sci

Numerical method for strongly variable-density flows at low Mach number: flame-sheet regularisation and a mass-flux immersed boundary method

This paper presents a robust numerical method for simulating strongly variable-density, low-Mach-number flows in combustion systems by integrating a fractional time-step model with flame-sheet regularisation and an extended mass-flux immersed boundary method to handle thermal gradients and complex burner geometries on Cartesian grids.

Matheus P. Severino, Fernando F. Fachini, Elmer M. Gennaro, Daniel Rodríguez, Leandro F. Souza2026-03-03🔬 physics

Graph neural network force fields for adiabatic dynamics of lattice Hamiltonians

This paper introduces a graph neural network framework that enforces lattice symmetries through message passing to achieve scalable, high-accuracy force fields for simulating the adiabatic dynamics of correlated lattice systems, enabling the discovery of anomalously slow coarsening in charge-density-wave ordering.

Yunhao Fan, Gia-Wei Chern2026-03-03🔬 cond-mat

Anisotropic two-dimensional magnetoexciton with exact center-of-mass separation

This paper presents an exact analytical framework for separating center-of-mass and relative motions in anisotropic two-dimensional magnetoexcitons, revealing new anisotropy-dependent couplings and providing precise, non-perturbative solutions for magnetoexciton properties in materials like monolayer black phosphorus and titanium trisulfide without relying on stationary-center-of-mass approximations.

Dang-Khoa D. Le, Hoang-Viet Le, Dai-Nam Le, Duy-Anh P. Nguyen, Thanh-Son Nguyen, Ngoc-Tram D. Hoang, Van-Hoang Le2026-03-03🔬 cond-mat.mes-hall

Convergent calculations of double ionization of helium: from ( $γ$ ,2e) to (e,3e) processes

This paper resolves conflicting interpretations of double ionization of helium by demonstrating that the original Kheifets et al. calculations remain valid and can be reproduced either by incorporating the second Born approximation or by using a corrected ground state similar to that proposed by Jones and Madison.

A. S. Kheifets, Igor Bray2026-03-03🔬 physics.atom-ph

Neuro-Symbolic AI for Analytical Solutions of Differential Equations

The paper introduces SIGS, a novel neuro-symbolic framework that automates the discovery of exact analytical solutions for differential equations—including coupled nonlinear PDEs—by combining formal grammars for syntactic validity with continuous latent space optimization to minimize physics-based residuals, thereby achieving significant improvements in accuracy and efficiency over existing methods.

Orestis Oikonomou, Levi Lingsch, Dana Grund, Siddhartha Mishra, Georgios Kissas2026-03-02🤖 cs.LG

Upscaling the Navier-Stokes-Cahn-Hilliard model for incompressible multiphase flow in inhomogeneous porous media

This paper rigorously derives a macroscopic two-phase flow model for inhomogeneous porous media by upscaling the Navier-Stokes-Cahn-Hilliard equations via volume averaging, incorporating wetting behavior into the averaged chemical potential and validating the framework through numerical simulations.

Chunhua Zhang, Peiyao Liu, Cheng Peng, Lian-Ping Wang, Zhaoli Guo2026-03-02🔢 math-ph

Bridging the Gap Between Virtual and Physical Laboratories: A Web-Based Interactive Platform for Undergraduate Physics Practicals

This paper presents a web-based interactive platform developed at St. Xavier's College, Kolkata, which successfully replicates physical physics laboratory setups to enhance undergraduate students' conceptual understanding and experimental confidence, as evidenced by overwhelmingly positive feedback from users.

Ashadul Halder, Shibaji Banerjee2026-03-02🔬 physics

Geometric Autoencoder Priors for Bayesian Inversion: Learn First Observe Later

This paper introduces Geometric Autoencoders for Bayesian Inversion (GABI), a framework that learns geometry-aware generative models from large datasets of varying physical systems to serve as informative priors for robust, well-calibrated uncertainty quantification in ill-posed inverse problems without requiring knowledge of governing equations.

Arnaud Vadeboncoeur, Gregory Duthé, Mark Girolami, Eleni Chatzi2026-03-02📊 stat

← Previous Next →

physics.comp-ph