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.

Dirac Fermions and Flat Bands in Phosphorus Carbide Nanotubes: Structural and Quantum Phase Transitions in a Quasi-One-Dimensional Material

This study predicts that phosphorus carbide nanotubes ( $\text{P}_2\text{C}_3$ NTs) are a stable, chemically realistic quasi-one-dimensional material that uniquely hosts coexisting Dirac fermions and robust flat bands at the Fermi level, while exhibiting strain-induced structural and quantum phase transitions, localized edge states, and tunable magnetism for potential applications in quantum hardware and spintronics.

Shivam Sharma, Chenhaoyue Wang, Hsuan Ming Yu, Amartya S. Banerjee2026-03-19🔬 cond-mat.mtrl-sci

Renormalization-Inspired Effective Field Neural Networks for Scalable Modeling of Classical and Quantum Many-Body Systems

This paper introduces Effective Field Neural Networks (EFNNs), a novel architecture leveraging continued functions from renormalization theory to accurately model classical and quantum many-body systems with superior generalization to larger lattice sizes and significant computational speedups compared to exact diagonalization and standard deep learning models.

Xi Liu, Yujun Zhao, Chun Yu Wan, Yang Zhang, Junwei Liu2026-03-19🔬 physics

A quantitative analysis of semantic information in deep representations of text and images

This paper employs Information Imbalance to demonstrate that semantic information converges across languages, modalities, and architectures in deep models, revealing that predictability is strongest in specific central or final layers and that larger, independently trained models can outperform jointly trained multimodal models in cross-modal alignment.

Santiago Acevedo, Andrea Mascaretti, Riccardo Rende, Matéo Mahaut, Marco Baroni, Alessandro Laio2026-03-19🔬 physics

Variational Learning of Physical Intuition from a Few Observations

This paper introduces a variational learning framework where small neural networks achieve robust generalization in predicting physical outcomes from just a few observations by approximating a solution manifold where the Euler-Lagrange operator is stationary, thereby establishing a principled route to artificial physical intuition.

Jingruo Peng, Shuze Zhu2026-03-19🔬 physics

Towards Unified AI-Driven Fracture Mechanics: The Extended Deep Energy Method (XDEM)

The paper introduces the Extended Deep Energy Method (XDEM), a unified deep learning framework that overcomes the limitations of existing approaches by integrating discrete and continuous fracture models to achieve accurate and efficient predictions using sparse collocation points.

Yizheng Wang, Yuzhou Lin, Somdatta Goswami, Luyang Zhao, Huadong Zhang, Jinshuai Bai, Cosmin Anitescu, Mohammad Sadegh Eshaghi, Xiaoying Zhuang, Timon Rabczuk, Yinghua Liu2026-03-19🔬 physics

Scalable Quantum Computational Science: A Perspective from Block-Encodings and Polynomial Transformations

This perspective article proposes block-encodings and polynomial transformations as a unified, scalable framework for quantum computational science, bridging the gap between abstract algorithmic theory and practical applications in chemistry, physics, and optimization.

Kevin J. Joven, Elin Ranjan Das, Joel Bierman, Aishwarya Majumdar, Masoud Hakimi Heris, Yuan Liu2026-03-19⚛️ quant-ph

Atomic forces from correlation energy functionals based on the adiabatic-connection fluctuation-dissipation theorem

This paper presents the implementation of analytical atomic forces for correlation energy functionals based on the adiabatic-connection fluctuation-dissipation theorem within the random phase approximation (RPA) and RPAx frameworks, demonstrating their high numerical accuracy and systematic improvement over standard DFT methods for predicting geometries, vibrational frequencies, and anharmonic phonon shifts in molecules and solids.

Damian Contant, Maria Hellgren2026-03-19🔬 cond-mat.mtrl-sci

A Flux-Correction Form of the Third-Order Edge-Based Scheme for a General Numerical Flux Function

This paper introduces a flux-correction form of the third-order edge-based scheme that enables the direct application of general numerical flux functions while preserving third-order accuracy through the use of the U-MUSCL scheme with $\kappa = 1/2$ , as verified by numerical results on irregular tetrahedral grids.

Hiroaki Nishikawa2026-03-19🔬 physics

Chaotic Oscillator Networks for Classification Tasks

This paper proposes a scalable machine learning framework for classification and pattern recognition that leverages ensembles of coupled chaotic oscillators, where a neural network automatically learns the necessary coupling terms to induce local resonances for data processing, thereby eliminating the need for expert-designed coupling rules and enabling efficient gradient-based optimization.

Toni Ivas, Georgios Violakis, Roland Richter, Patrik Hoffmann, Sergey Shevchik2026-03-19🌀 nlin

Rejection-free Glauber Monte Carlo for the 2D Random Field Ising Model via Hierarchical Probabilistic Counters

This paper introduces an efficient, rejection-free Monte Carlo algorithm that combines Glauber dynamics with hierarchical probabilistic counters to simulate the 2D Random Field Ising Model in $O(\log N)$ time, achieving speedups of over two orders of magnitude compared to the Metropolis method in low-temperature regimes while enabling proper dynamical studies of disordered spin systems.

Luca Cattaneo, Federico Ettori, Giovanni Cerri, Paolo Biscari, Ezio Puppin2026-03-19🔬 cond-mat

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