903 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 introduces a machine-learning framework for coarse-grained molecular dynamics that augments traditional force matching with stochastic Hessian-vector product matching to incorporate second-order curvature information, significantly improving the accuracy and transferability of coarse-grained potentials for biomolecular simulations.
This paper investigates how the choice of time integrator, the addition of numerical dissipation, and the problem representation influence the efficiency and stability of quantized tensor train (QTT) calculations for advection-dominated problems, aiming to mitigate error accumulation and rank growth in long-time simulations.
This paper demonstrates that replacing finite-difference approximations with forward-mode Automatic Differentiation for Jacobian-vector products in Jacobian-Free Newton-Krylov solvers significantly enhances both computational performance (by 2–3 orders of magnitude) and global robustness (increasing completion rates from 42% to 95%) across diverse nonlinear problems and hardware architectures.
This study utilizes finite element simulations to demonstrate that in non-Newtonian power-law fluids, shear-thinning behavior enhances heat transfer while uniform thermal boundary conditions promote stronger convection and higher entropy generation compared to non-uniform heating, offering key insights for optimizing thermal system design.
This paper proposes a macroscopic mesoscopic, deterministic stochastic coupling strategy that integrates higher-order constitutive relations and chemical reaction source terms sampled from DSMC into a macroscopic synthetic equation to accelerate simulations, achieve noise reduction, and overcome computational bottlenecks in near-continuum chemical reaction flows.
The paper introduces Elastica++, an open-source, high-performance framework that utilizes the Cosserat-rod model and shared-memory parallelism to enable large-scale, multiphysics simulations of interacting slender structures across diverse applications ranging from soft robotics to active matter.
This paper presents a hybrid quantum-classical method for solving the advection-diffusion equation that scales efficiently with system dimension and demonstrates reliable results on current noisy IBM quantum hardware, offering a potential pathway to overcome computational and power limitations in numerical weather prediction.
This study leverages machine learning and molecular dynamics to identify potential inhibitors targeting Gram-negative bacterial resistance mechanisms, specifically RND efflux pumps and erythromycin esterases, aiming to overcome the limitations of existing FDA-approved antibacterial drugs.
This paper introduces APRIL, a framework that augments supervised loss with auxiliary physically-redundant terms to improve convergence and accuracy in parameter estimation for large multi-system datasets, demonstrating up to an order-of-magnitude performance gain in gravitational wave parameter estimation compared to standard approaches.
This paper introduces a structure-preserving low-rank compression protocol for two-electron reduced density matrices that reduces memory scaling from quartic to quadratic while maintaining chemical accuracy, thereby enabling efficient application of eigenvector continuation workflows to large-scale nonadiabatic molecular dynamics simulations.