922 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.
This paper extends high-temperature series expansions to dynamic Matsubara spin correlators for Heisenberg models, providing precomputed exact expansion coefficients up to the 12th order for arbitrary lattices to enable the calculation of static susceptibilities and real-frequency dynamic structure factors.
Using the TAMS rare-event algorithm on a coupled conceptual model, this study quantifies the probability of a tipping cascade from an AMOC collapse to Amazon rainforest degradation, revealing that while such a transition in northwest Brazil within 200 years is highly unlikely, it is strictly contingent upon a prior AMOC collapse that induces severe drying and wildfire risks.
This paper investigates persistent charge and spin currents in a ferromagnetic Hatano-Nelson ring, demonstrating how non-reciprocal hopping induces a non-Hermitian Aharonov-Bohm effect and revealing that disorder can surprisingly amplify spin transport across various topological and parameter regimes.
This paper challenges conventional noise-mitigation strategies by demonstrating that preserving biased, non-unital noise in variational quantum algorithms can actually enhance classical optimization and yield better solutions, whereas standard twirling techniques that symmetrize noise often degrade performance.
This paper presents a validated numerical model within the PELOTON code that simulates pellet rocket acceleration in thermonuclear fusion devices by accounting for ablation cloud asymmetry and plasma gradients, demonstrating consistency with JET experimental trajectories and revealing reduced deviation for deuterium-neon composite pellets.
This paper proposes Streaming Operator Inference, a non-intrusive model reduction framework that utilizes incremental SVD and recursive least squares to learn accurate reduced-order models from sequential data streams, thereby overcoming the memory limitations of traditional batch methods and enabling online adaptation for large-scale dynamical systems.
This tutorial-style article provides self-directed computational exercises and reproducible Jupyter notebooks to facilitate the practical application of generalized profiling, a penalized likelihood method for parameter estimation in ordinary differential equation models.
The paper introduces lrux, a high-performance JAX-based software package that accelerates quantum Monte Carlo algorithms by efficiently computing low-rank updates of determinants and Pfaffians, reducing computational complexity from to and achieving up to speedups on GPUs.
This paper presents a polymer self-consistent field theory for atoms using Gaussian basis functions and an excluded-volume model to enforce the Pauli-exclusion principle, successfully calculating binding energies and electron densities for elements from hydrogen to krypton while recovering standard quantum mechanical limits.
This ab initio study of PbTe/Pb heterostructures reveals that while proximity-induced superconductivity with anisotropic pairing emerges, a large Schottky barrier in the normal state likely prevents the formation of Majorana zero modes, challenging the viability of these specific interfaces for topological quantum computing.