1190 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 serves as a concise handbook summarizing numerically exact algorithms for treating fermion determinants in quantum Monte Carlo simulations, specifically distinguishing between stable dense matrix methods for small spatial volumes at low temperatures and efficient sparse matrix approaches for larger volumes.
This paper utilizes the recently developed small-tensor-product (STP) decomposition framework to perform numerically exact relativistic full configuration interaction calculations in a 100-orbital space, thereby establishing a definitive benchmark with rigorous error bounds to assess the accuracy, variationality, and convergence of relativistic coupled cluster and density matrix renormalization group methods.
Using the high-fidelity M3D-C1 code, this study investigates low-n MHD instabilities in SPARC baseline scenarios, revealing that internal kink modes drive sawtooth crashes through magnetic reconnection and pressure mixing, with crash timing highly sensitive to the on-axis safety factor and temperature profiles.
This paper introduces Magboltz-GUI, a Python-based open-source graphical user interface designed to simplify the workflow of the Magboltz simulation tool for defining gas mixtures, configuring parameters, and visualizing results, thereby making electron transport property calculations more accessible for research and teaching in gaseous detector applications.
This paper demonstrates that purely deterministic interactions in the logistic Game of Life can generate scale-invariant dynamics and criticality, revealing two distinct critical points that separate sparse-static, sparse-dynamic, and dense-dynamic phases with unconventional power-law cluster distributions.
This paper introduces a "freeze-and-release" direct optimization method that successfully achieves variational orbital optimization for excited electronic states, particularly charge transfer excitations, by preventing variational collapse and correctly describing energy dependencies without requiring long-range exact exchange, where conventional maximum overlap methods often fail.
This living document explores the rapidly evolving potential of quantum computing in Thermal Science, initially focusing on heat conduction as a paradigmatic test case to develop novel algorithms and evaluate real hardware performance in the pursuit of quantum supremacy for engineering applications.
This paper presents a general methodology for estimating parameters in stochastic oscillatory systems by minimizing a novel weighted cost function—incorporating power spectral density, analytic signal, and position crossings—using differential evolution, which is validated on test data and applied to a biophysical model of auditory mechanics.
This paper introduces Point Group Order Parameters (PGOPs) as a new set of metrics to directly quantify local point-group symmetry in complex particle systems, demonstrating their superior utility in detecting crystalline order compared to traditional bond-orientational parameters and providing their implementation in the open-source SPATULA software package.
This paper proposes a method to incorporate lattice-reflection symmetry into the tensor-network renormalization group (TNRG) with entanglement filtering for both two and three dimensions by defining the symmetry in tensor-network language and introducing a transposition trick to preserve it during key operations, thereby enabling the separate extraction of scaling dimensions in specific symmetry sectors.