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.

ADI schemes for heat equations with irregular boundaries and interfaces in 3D with applications

This paper proposes and rigorously analyzes efficient, unconditionally stable, and second-order accurate Alternating Direction Implicit (ADI) schemes, enhanced with a kernel-free boundary integral method and level set technique, to solve three-dimensional heat equations and reaction-diffusion problems featuring irregular boundaries, interfaces, and free boundaries such as those in dendritic solidification.

Han Zhou, Minsheng Huang, Wenjun Ying2026-04-20🔬 physics

Fractal geometry-governed oxygen diffusion: Tumors vs. Normal Tissues

This paper proposes a fractal geometry-governed diffusion-reaction model to explain differential tissue responses to FLASH ultra-high dose rate irradiation, demonstrating that structural heterogeneity and anomalous subdiffusive dynamics significantly suppress long-range oxygen transport and create isolated reactive domains compared to classical Euclidean diffusion.

Neda Valizadeh, Robabeh Rahimi, Ramin Abolfath2026-04-20🌀 nlin

A Structure-Preserving Graph Neural Solver for Parametric Hyperbolic Conservation Laws

This paper presents an interpretable, structure-preserving graph neural solver that integrates classical numerical principles with graph neural networks to achieve stable, accurate, and computationally efficient long-horizon predictions for parametric hyperbolic conservation laws while inherently respecting physical properties like local conservation and upwinding.

Jiamin Jiang, Shanglin Lv, Jingrun Chen2026-04-20🔬 physics

PINNACLE: An Open-Source Computational Framework for Classical and Quantum PINNs

The paper introduces PINNACLE, an open-source framework that unifies classical and quantum physics-informed neural networks (PINNs) with advanced training strategies and multi-GPU acceleration, providing a comprehensive benchmark study to evaluate their performance, scalability, and trade-offs against traditional solvers.

Shimon Pisnoy, Hemanth Chandravamsi, Ziv Chen, Aaron Goldgewert, Gal Shaviner, Boris Shragner, Steven H. Frankel2026-04-20🤖 cs.LG

Probabilistic Upscaling of Hydrodynamics in Geological Fractures Under Uncertainty

This study introduces a scalable probabilistic workflow that integrates Bayesian correction and deep learning surrogates to bridge image-based fracture geometries with uncertainty-aware hydraulic predictions, effectively capturing the impact of aperture heterogeneity and 3D void complexity on transmissivity while avoiding the computational cost of repeated high-fidelity simulations.

Sarah Perez, Florian Doster, Hannah Menke, Ahmed ElSheikh, Andreas Busch2026-04-20🔬 physics

Machine learning isotope shifts in molecular energy levels

This paper presents a machine learning framework that corrects isotopologue extrapolation errors in molecular energy levels using neural networks for CO $_2$ and a novel transfer learning approach for CO, thereby significantly improving the accuracy of spectroscopic line lists essential for exoplanet atmospheric studies.

Marco G. Barnfield, Oleg L. Polyansky, Sergei N. Yurchenko, Jonathan Tennyson2026-04-20🔭 astro-ph

Quantum-Inspired Simulation of 2D Turbulent Rayleigh-Bénard Convection

This paper demonstrates that Matrix Product State (MPS) methods can efficiently simulate 2D turbulent Rayleigh-Bénard convection up to Rayleigh numbers of $10^{10}$ , achieving accurate statistical observables with significantly fewer degrees of freedom than traditional methods and suggesting scalability for investigating the ultimate regime of turbulence.

Nis-Luca van Hülst, Mario Guillaume Cecile, Hai-Yen Van, Tomohiro Hashizume, Eugene de Villiers, Dieter Jaksch2026-04-20🔬 physics

Driven spin dynamics enhances cryptochrome magnetoreception: Towards live quantum sensing

This paper demonstrates that driving the spin dynamics of strongly coupled radical pairs in cryptochrome through modulated inter-radical distances overcomes sensitivity suppression and significantly enhances geomagnetic field detection via Landau-Zener transitions, suggesting that "live" dynamic magnetoreceptors are more sensitive than static ones.

Luke D. Smith, Farhan T. Chowdhury, Iona Peasgood, Nahnsu Dawkins, Daniel R. Kattnig2026-04-17⚛️ quant-ph

Bayesian electron density determination from sparse and noisy single-molecule X-ray scattering images

This paper presents a rigorous Bayesian approach that successfully determines electron densities of small proteins from sparse and noisy single-molecule X-ray scattering images by overcoming limitations such as low photon counts, unknown molecular orientations, and various experimental artifacts.

Steffen Schultze, Helmut Grubmüller2026-04-17🔬 physics

Full- and low-rank exponential Euler integrators for the Lindblad equation

This paper introduces novel full- and low-rank exponential Euler integrators for the Lindblad equation that unconditionally preserve the physical properties of positivity and trace while offering sharp error estimates and superior performance compared to existing methods.

Hao Chen, Alfio Borzì, Denis Janković, Jean-Gabriel Hartmann, Paul-Antoine Hervieux2026-04-17⚛️ quant-ph

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