1259 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 uses particle-in-cell simulations to investigate how time-variant injection profiles influence space-charge-limited current density, suggesting that time-varying injection can further enhance electron transport during short-pulse conditions.
This paper develops an efficient, high-order compact gas-kinetic scheme in an arbitrary Lagrangian-Eulerian (ALE) formulation that achieves high spatial and temporal accuracy through a single-stage time-accurate flux evolution and a simplified fourth-order reconstruction to minimize the computational costs associated with mesh motion.
This paper demonstrates that the Wave-Particle Turbulent Simulation (WPTS) framework, when coupled with a mixing-length-based characteristic-time closure, accurately predicts the similarity behavior and characteristic features of spatially developing round jets across different Reynolds numbers.
This paper presents a machine learning approach using Gaussian process regression and long-range descriptors to predict electron densities in large-scale moiré superlattices, enabling the efficient modeling of complex quantum phenomena that were previously computationally prohibitive for traditional ab initio methods.
This paper introduces a novel, fully differentiable simulation framework that unifies computational fluid dynamics and machine learning, enabling end-to-end optimization and data-driven modeling for multi-scale flows across both continuum and rarefied regimes.
This paper develops a new analytical framework by recasting the viscous heat equations into solvable biharmonic equations, enabling the study of complex hydrodynamic phenomena such as thermal vortices and heat backflow in both phonon and electron systems.
This paper provides direct experimental evidence and atomic-scale simulations demonstrating that light reduces dislocation mobility in zinc sulfide by increasing Peierls stress and enhancing dislocation core stress fields, thereby offering a mechanism for light-modulated photo-plasticity in inorganic semiconductors.
This paper demonstrates that applying an optimal parameterization strategy—which uses estimated local mean first passage times to guide trajectory pruning and replication—significantly reduces the variance and improves the reliability of weighted ensemble simulations for complex, high-dimensional molecular folding processes.
This paper introduces the -Gauss-Logistic map, a new nonlinear dynamical system that exhibits a transition from gradual period-doubling cascades to chaos for to an abrupt onset of chaos without bifurcations for .
This paper presents a novel volume penalization technique that uses a perturbation approach to efficiently and accurately simulate acoustically-actuated flows in complex microchannels by segregating the problem into harmonic first-order and time-averaged second-order systems.