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.

Fast and Accurate Prediction of Lattice Thermal Conductivity via Machine Learning Surrogates

This paper benchmarks 15 machine learning surrogate models on a large Phonix database to predict lattice thermal conductivity, revealing that while MLIP-embedded models excel in interpolation, deep neural networks like ALiEGNN offer superior robustness for out-of-distribution extrapolation, thereby enabling efficient high-throughput screening of thermoelectric materials at a fraction of the computational cost of first-principles simulations.

Zeyu Wang, Shuya Yamazaki, Martin Hoffmann Petersen, Masato Ohnishi, Tomiya Yamamoto, Wei Nong, Jianghai Wang, Ruiming Zhu, Masatoshi Hanai, Michimasa Morita, Toyotaro Suzumura, Zekun Ren, Junichiro S (…)2026-05-13🔬 cond-mat.mtrl-sci

Identifying the relevant parameters in design strategies for stable glasses

This study challenges the assumption that optimizing specific physical properties like hyperuniformity or local ordering causes enhanced glass stability, demonstrating instead that the dynamical process of changing particle diameters is the true causal factor behind the formation of ultrastable glasses.

Leonardo Galliano, Ludovic Berthier2026-05-13🔬 cond-mat

Machine Learning for neutron source distributions

This paper proposes and evaluates a novel approach using probabilistic generative models to estimate neutron source distributions from Monte Carlo particle lists, enabling efficient, memory-independent sampling once the models are trained.

Jose Ignacio Robledo, Norberto Schmidt, Klaus Lieutenant, Jingjing Li, Stefan Kesselheim, Paul Zakalek2026-05-13🔬 physics

Designing Coulombic Contact Interactions between Polarizable Particles through Asymmetry

This paper demonstrates that by jointly tuning the size, charge, and dielectric asymmetries of polarizable particles, complex contact electrostatic interactions can be engineered to reduce to simple Coulombic behavior, thereby enabling the design of self-assembling materials with predictable structures.

Yanyu Duan, Zecheng Gan2026-05-13🔬 cond-mat

Bayesian Reasoning for Physics Informed Neural Networks

This paper introduces an evidence-driven Bayesian formulation of Physics-Informed Neural Networks that utilizes a Laplace approximation to analytically compute model evidence, enabling efficient, sampling-free automatic optimization of loss weights and uncertainty quantification across various partial differential equations.

Krzysztof M. Graczyk, Kornel Witkowski2026-05-12🔬 physics

Acoustics-based Active Control of Unsteady Flow Dynamics using Reinforcement Learning Driven Synthetic Jets

This paper presents a deep reinforcement learning framework that utilizes far-field acoustic measurements as the primary feedback signal to drive synthetic jet actuation, successfully suppressing unsteady wake dynamics behind a circular cylinder and achieving significant reductions in radiated noise and drag without relying on traditional velocity or pressure sensors.

Siddharth Rout, Khai Phan, Chao-An Lin2026-05-12🔬 physics.app-ph

Diagnosing phase transitions through time-scale entanglement

This paper introduces time-scale entanglement, a novel form of entanglement between imaginary time scales accessible via quantics tensor train diagnostics (QTTD), as a universal and unbiased indicator that is generically enhanced near phase transitions and becomes scale-invariant at quantum critical points.

Stefan Rohshap, Hirone Ishida, Frederic Bippus, Leonard M. Verhoff, Anna Kauch, Karsten Held, Hiroshi Shinaoka, Markus Wallerberger2026-05-12🔬 cond-mat

A Quantum Linear Systems Pathway for Solving Differential Equations

This paper presents a systematic quantum pathway for solving differential equations by combining block encoding with Quantum Singular Value Transformation (QSVT), demonstrating its application to heat and Burgers' equations while providing critical hardware resource estimates and scaling analyses that highlight current limitations and future directions for achieving quantum advantage.

Abhishek Setty2026-05-12⚛️ quant-ph

Consistent Projection of Langevin Dynamics: Preserving Thermodynamics and Kinetics in Coarse-Grained Models

This paper presents a projection-based coarse-graining formalism for underdamped Langevin dynamics that integrates generator Extended Dynamic Mode Decomposition (gEDMD) and thermodynamic interpolation to accurately preserve both the thermodynamic and kinetic properties of complex multi-scale systems across different thermodynamic states.

Vahid Nateghi, Lara Neureither, Selma Moqvist, Carsten Hartmann, Simon Olsson, Feliks Nüske2026-05-12🔬 physics

Crystal Fractional Graph Neural Network for Energy Prediction of High-Entropy Alloys

This paper proposes a Crystal Fractional Graph Neural Network that combines local atomic environment analysis via graph attention mechanisms with global compositional data to accurately predict the energy of high-entropy alloys, achieving first-principles-level precision on a dataset of over 1,000 structures while acknowledging current limitations with large crystal cells.

Takanori Kotama, Yang Huang2026-05-12🔬 physics

← Previous Next →