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.

Phenomenological energy exchange of diatomic gases: Comparison of Pullin and Borgnakke-Larsen models in direct simulation Monte Carlo method

This study compares the widely used Borgnakke-Larsen model with the more theoretically rigorous Pullin model for simulating translational-rotational energy exchange in diatomic gases using the DSMC method, demonstrating that the Pullin model provides a more consistent physical foundation while maintaining comparable efficiency to the BL model in highly rarefied flows.

Hao Jin, Sha Liu, Ningchao Ding, Sirui Yang, Huahua Cui, Congshan Zhuo, Chengwen Zhong2026-02-10🔬 physics

Compressed Sensing Methods for Memory Reduction in Monte Carlo Simulations

This paper demonstrates that using compressed sensing with overlapping cells can significantly reduce the memory requirements of high-fidelity Monte Carlo neutronic simulations, achieving up to 96.25% memory savings in 3D reconstructions while maintaining high accuracy.

Ethan Lame, Camille Palmer, Todd Palmer, Ilham Variansyah2026-02-10🔬 physics

Field conserving adaptive mesh refinement (AMR) scheme on massively parallel adaptive octree meshes

This paper proposes a scalable, field-conserving coarsening operator for parallel octree-based adaptive mesh refinement that uses an $L^2$ projection to prevent systematic drift in conserved quantities during long-horizon simulations of partial differential equations.

Kumar Saurabh, Makrand A. Khanwale, Masado Ishii, Hari Sundar, Baskar Ganapathysubramanian2026-02-10🔢 math-ph

A quantum-inspired multi-level tensor-train monolithic space-time method for nonlinear PDEs

This paper proposes a multilevel tensor-train (TT) framework for solving nonlinear partial differential equations in a monolithic space-time formulation, utilizing a coarse-to-fine strategy to provide robust initializations for Newton iterations and achieve superior convergence and efficiency across diverse diffusive, convective, and dispersive dynamics.

N. R. Rapaka, R. Peddinti, E. Tiunov, N. J. Faraj, A. N. Alkhooori, L. Aolita, Y. Addad, M. K. Riahi2026-02-10⚛️ quant-ph

Extracting Many-Body Quantum Resources within One-Body Reduced Density Matrix Functional Theory

This paper establishes a novel framework within One-Body Reduced Density Matrix Functional Theory that enables the universal determination of Quantum Fisher Information for fermionic and bosonic ground states directly from the one-body reduced density matrix, thereby avoiding the computational complexity of exponentially large wave functions.

Carlos L. Benavides-Riveros, Tomasz Wasak, Alessio Recati2026-02-09🔬 cond-mat

A Nonlocal Orientation Field Phase-Field Model for Misorientation- and Inclination- Dependent Grain Boundaries

This paper proposes a nonlocal orientation field phase-field model that incorporates misorientation- and inclination-dependent grain boundary anisotropy using a single orientation field, thereby enabling precise tuning of grain boundary energy while simplifying the fitting procedure and accurately reproducing key microstructural behaviors like linear grain growth and triple junction equilibrium.

Xiao Han, Axel van de Walle2026-02-09🔬 cond-mat.mtrl-sci

Comparison of inviscid and viscous vortex shedding from translating and rotating plates

This study compares an inviscid vortex sheet model with Navier-Stokes simulations across approximately 70 unsteady plate motions at moderate Reynolds numbers, demonstrating that the inviscid approach accurately predicts forces and flow structures in body-dominated regimes while showing reduced accuracy at low angles of attack in flow-dominated configurations.

Yu Jun Loo, Silas Alben2026-02-09🔬 physics

A Demonstration of a Neural Network as a Bridge Between Galaxy Simulations and Surveys

This paper demonstrates that a simple, single-hidden-layer neural network trained on synthetic galaxies from the SHARK semi-analytic model can accurately predict stellar masses for real GAMA survey galaxies using only absolute magnitudes and color indices, achieving a scatter of ~0.131 dex and proving that complex deep-learning architectures are unnecessary for robust simulation-to-observation transfer in galaxy evolution studies.

E. Elson2026-02-09🔭 astro-ph

Generalized Finite Differences Method Applied to Finite Photonic Crystal

This paper proposes a Generalized Finite-Differences in the Frequency Domain method that discretizes a fundamental domain to compute photonic band structures for finite photonic crystals, demonstrating its validity on a one-dimensional crystal in an optical cavity while analyzing the transition to infinite systems.

Santiago Bustamante, Esteban Marulanda, Jorge Mahecha, Herbert Vinck2026-02-06🔬 physics.optics

TTNOpt: Tree tensor network package for high-rank tensor compression

The paper introduces TTNOpt, a software package that leverages tree tensor networks to efficiently compute ground states and physical properties of quantum spin systems while also performing high-rank tensor compression for high-dimensional data analysis by optimizing network structures based on entanglement patterns.

Ryo Watanabe, Hidetaka Manabe, Toshiya Hikihara, Hiroshi Ueda2026-02-06🔬 cond-mat

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