🔬 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.

Benchmarking of Massively Parallel Phase-Field Codes for Directional Solidification

This paper presents a comprehensive benchmark comparing a GPU-accelerated finite-difference phase-field code (GPU-PF) and a CPU-parallelized finite-element adaptive-mesh code (PRISMS-PF) for simulating directional solidification of Al-Cu and SCN-camphor alloys under experimentally relevant conditions, validating their accuracy in predicting dendritic morphology and tip dynamics while evaluating their computational performance to support integrated computational materials engineering workflows.

Jiefu Tian, David Montiel, Kaihua Ji, Trevor Lyons, Jason Landini, Katsuyo Thornton, Alain Karma2026-04-30🔬 cond-mat.mtrl-sci

qFHRR: Rethinking Fourier Holographic Reduced Representations through Quantized Phase and Integer Arithmetic

The paper introduces qFHRR, a quantized phase formulation of Fourier Holographic Reduced Representations that replaces floating-point arithmetic with integer-only modular operations to significantly reduce memory footprint and enable efficient hardware implementation while preserving the algebraic properties and high-fidelity similarity structure of the original complex-valued framework.

Shay Snyder (George Mason University), Hamed Poursiami (George Mason University), Maryam Parsa (George Mason University)2026-04-30🔬 physics

From Code to Figure: A FAIR-Aligned Data Provenance Chain for Reproducible Simulation Research in Numerical Physics

This paper presents an integrated, FAIR-aligned workflow that combines version control, automated testing, structured logging, and standardized post-processing to establish a complete data provenance chain ensuring reproducibility from code development to published figures in numerical physics simulations.

Markus Uehlein, Tobias Held, Christopher Seibel, Lukas G. Jonda, Baerbel Rethfeld, Sebastian T. Weber2026-04-30🔬 physics

Accelerating finite-element-based projector augmented-wave density functional theory calculations with scalable GPU-centric computational methods

This paper presents a scalable, GPU-centric finite-element projector augmented-wave (PAW-FE) method that leverages algorithmic innovations like mixed-precision arithmetic and Chebyshev filtered subspace iteration to achieve significant speedups and exascale-ready performance for large-scale, chemically accurate density functional theory simulations.

Kartick Ramakrishnan, Phani Motamarri2026-04-30🔬 physics

Implementation of the hybrid exchange-correlation functionals in the SIESTA code

This paper presents an efficient and accurate implementation of hybrid exchange-correlation functionals in the SIESTA code, utilizing a Gaussian-fitted representation of numerical atomic orbitals to enable large-scale, scalable simulations of extended systems with significantly improved band gap predictions.

Yann Pouillon, Bill Clintone Oyomo, James Sifuna, María Camarasa-Gómez, Xinming Qin, Carlos Beltrán, Fernando Gómez-Ortiz, Honghui Shang, Javier Junquera2026-04-30🔬 cond-mat.mtrl-sci

Mixture of Experts Framework in Machine Learning Interatomic Potentials for Atomistic Simulations

This paper introduces a multifidelity Mixture-of-Experts framework for machine learning interatomic potentials that spatially partitions simulation domains and employs a co-training strategy to resolve mechanical mismatches at interfaces, thereby achieving high-fidelity accuracy for complex catalytic systems at more than double the computational speed of standard methods.

Gabriel de Miranda Nascimento, Marc L. Descoteaux, Laura Zichi, Chuin Wei Tan, William C. Witt, Nicola Molinari, Sriteja Mantha, Daniil Kitchaev, Mordechai Kornbluth, Karim Gadelrab, Charles Tuffile (…)2026-04-30🔬 physics

Scaling in Supersonic Turbulence: Energy Spectra and Fluxes using High-Fidelity Direct Numerical Simulations

Using high-resolution GPU-accelerated direct numerical simulations, this study reveals that supersonic turbulence undergoes a fundamental shift in energy cascade mechanisms, characterized by a transition from Kolmogorov-like to Burgers-like scaling in rotational energy spectra driven by dominant cross-scale energy transfer from solenoidal to compressive modes.

Harshit Tiwari, Dhananjay Singh, Mahendra K. Verma, Rajesh Ranjan2026-04-30🔬 physics

Drift-Free Conservative Dynamics from Quantized Interaction Rules

This contribution presents a framework for conservative dynamics situated at the operator level, which employs exact antisymmetric integer transfer rules on a quantized state space to eliminate numerical rounding drift and enforce entropy selection directly at the arithmetic level, thereby preserving conservation laws and shock structures without relying on approximate flux cancellation.

Park Junhu, Youngsoo Ha, Myungjoo Kang2026-04-30🔬 physics

Generalized Yee methods: Scalable symplectic finite element Maxwell solvers

This paper introduces Generalized Yee Methods (GYMs), a scalable class of structure-preserving finite element Maxwell solvers that extend Yee's method to unstructured meshes and higher-order accuracy by utilizing de Rham-conforming elements and sparse mass matrix approximations while rigorously maintaining locality and symplecticity for long-time numerical stability and particle-in-cell coupling.

Alexander S. Glasser, Hong Qin2026-04-29🔬 physics