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.

Sparse Müntz--Szász Recovery for Boundary-Anchored Velocity Profiles: A Short-Record Roughness Diagnostic in Turbulence

This paper introduces a sparse convex-relaxation framework using a Müntz–Szász/Jacobi dictionary to estimate effective local scaling exponents from short, boundary-anchored velocity profiles, demonstrating its utility as a finite-scale geometric diagnostic that reveals directional anisotropy and vorticity-aligned roughness structures in turbulent flows without requiring external calibration.

D Yang Eng2026-04-01🌀 nlin

Boltzmann Equation Solver for Thermalization

This paper introduces BEST, a parallelized Python framework that solves the momentum-resolved Boltzmann equation for arbitrary scattering processes using VEGAS Monte Carlo integration, while addressing a critical subtlety in handling identical particles to ensure exact energy conservation and validate the method through thermalization studies of massive scalar fields.

Jong-Hyun Yoon2026-04-01⚛️ hep-ph

The Closure Challenge: a benchmark task for machine learning in turbulence modelling

This paper introduces "The Closure Challenge," a standardized open-source benchmark comprising curated datasets, evaluation code, and specific test cases designed to establish a common metric for evaluating and advancing machine learning models in Reynolds-averaged Navier-Stokes turbulence modeling.

Ryley McConkey, Tyler Buchanan, Tess Smidt, Abigail Bodner, Richard Dwight, Paola Cinnella2026-04-01🔬 physics

Process-tensor approach to full counting statistics of charge transport in quantum many-body circuits

This paper introduces a numerical tensor-network method based on the process tensor to compute full counting statistics of charge transport in interacting one-dimensional quantum systems, successfully benchmarking the approach on the XXZ spin chain to recover known transport exponents and confirm the breakdown of Kardar-Parisi-Zhang universality in higher-order cumulants at the isotropic point.

Hari Kumar Yadalam, Mark T. Mitchison2026-04-01⚛️ quant-ph

Hydrogen-helium immiscibility boundary in planets

By employing large-scale molecular dynamics with machine learning potentials to map the hydrogen-helium immiscibility boundary, this study reveals that demixing temperatures are approximately 2000 K lower than previous estimates, suggesting that helium rain is plausible in Saturn but unlikely in Jupiter's warmer interior.

Xiaoyu Wang, Sebastien Hamel, Bingqing Cheng2026-04-01🔭 astro-ph

Thomas-Fermi equation revisited: A variation on a theme by Majorana

This paper revisits Majorana's method of converting the second-order Thomas-Fermi equation into a first-order one, applying it to both neutral and weakly ionized atoms to efficiently recalculate and validate key atomic physics quantities against previous numerical results.

Berthold-Georg Englert2026-04-01🔬 physics.atom-ph

Open Quantum Systems from Dynamical Constraints

This paper proposes a novel framework for open quantum systems where the environment emerges dynamically from the activation of constraints within a Dirac-quantized system, thereby encoding system-environment coupling directly in the constraint structure rather than through an explicit interaction Hamiltonian.

Yu Su, Yao Wang2026-04-01⚛️ quant-ph

Solving the (Navier-)Stokes equations with space and time adaptivity using deal.II

This paper demonstrates the flexibility and modularity of the deal.II library's multigrid infrastructure by solving stationary Stokes, transient Stokes, and incompressible Navier-Stokes equations using various adaptive strategies, including hp-adaptivity, space-time finite elements, and monolithic solvers.

Peter Munch, Marc Fehling, Martin Kronbichler, Nils Margenberg, Laura Prieto Saavedra2026-04-01🔬 physics

Gap edge eigenpairs from density matrix purification using moments of the Dirac distribution

This paper proposes a robust and efficient method to resolve band gap edge eigenstates by decomposing occupation number variance into particle and hole moments, which are then isolated via power narrowing iterations applied to the quasi-purified one-particle density matrix.

Lionel Alexandre Truflandier2026-04-01🔬 cond-mat.mtrl-sci

A Precision Emulation Approach to the GPU Acceleration of Ab Initio Electronic Structure Calculations

This paper demonstrates that using INT8-based emulation via the SCILIB-Accel tool can accelerate FP64 electronic structure calculations on modern GPUs without code modifications, achieving simultaneous improvements in performance and accuracy through tunable precision strategies.

Hang Liu, Junjie Li, Yinzhi Wang, Niraj K. Nepal, Yang Wang2026-04-01🔬 physics

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