1255 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 proposes a qudit-based formulation of the Rodeo algorithm featuring a novel "Rodeo kernel" spectral filter and a microcanonical protocol for estimating entropic quantities, demonstrating through numerical simulations on the Ising model that higher-dimensional ancillae significantly reduce fluctuations and enhance spectral analysis compared to traditional qubit implementations.
This paper presents a multiscale computational framework based on the Lagrangian Heterogeneous Multiscale Method that successfully links microstructural parameters, such as crosslink density and network connectivity, to the macroscopic rheological and mechanical properties of pressure-sensitive adhesives, offering a predictive tool for their rational design and optimization.
This study demonstrates that engineering nitrogen termination on diamond surfaces significantly enhances Cu/diamond interfacial thermal conductance by 21% through surficial mass modification and bonding regulation that selectively modulates high-frequency phonon transport, offering a promising non-metallic strategy to overcome interfacial limitations in Cu-diamond composites.
This paper introduces a novel, training-free graph theory-based algorithm that accurately segments and measures 3D detonation cells from pressure traces, overcoming the limitations of traditional manual and 2D methods to provide a robust tool for analyzing complex cellular geometries in detonation research.
This paper introduces a gridless quasistatic algorithm implemented in the Wake-T code that enables efficient, high-resolution simulation of axially symmetric plasma wakes for both laser- and beam-driven accelerators, significantly reducing computational costs compared to traditional 3D particle-in-cell methods.
This paper introduces a computationally efficient postprocessing method that utilizes differentiated Riemann variables to accurately reconstruct sub-cell wave geometry and sharpen contact discontinuities in 1D Euler shock-capturing simulations, outperforming existing high-resolution schemes in both accuracy and stability for challenging problems like the LeBlanc shock tube.
This study validates a neural network-based numerical model that utilizes 3D discontinuous Galerkin seismic simulations to directly estimate water volume in porous reservoirs, demonstrating its effectiveness through field experiments in Laukaa, Finland, to advance sustainable groundwater management.
This paper introduces SympFlow, a time-dependent symplectic neural network that leverages parameterized Hamiltonian flow maps to ensure energy and momentum conservation for both modeling known systems and discovering unknown dynamics from sparse data, backed by rigorous theoretical analysis and superior performance in long-term simulations.
This paper presents a novel statistical framework that leverages quantum mechanical concepts, specifically density operators and measurement theory, to achieve a symmetry-invariant, data-driven closure for partial differential equations, demonstrating its accuracy in modeling unresolved degrees of freedom for the shallow water equations.
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