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 proposes a multi-fidelity surrogate modeling framework using recursive co-kriging to accurately and efficiently predict wind-load coefficients on modern container ships by combining empirical correlations with simplified and detailed CFD models across various harbor environments.
The paper introduces PHIN-GAN, a physics-informed generative adversarial network that utilizes analytical probability density functions of the Landau straggling function to provide high-fidelity, scalable, and computationally efficient simulations of particle-matter interactions compared to traditional methods like GEANT4.
This study demonstrates that even in the continuum regime, low-Reynolds-number viscous shock tubes exhibit significant non-equilibrium effects during shock-boundary layer interactions, necessitating multiscale methods like the unified gas-kinetic scheme (UGKS) over conventional Navier-Stokes solvers.
This paper introduces two new variants of the structure factor twist averaging (sfTA) method, "paired sfTA" and "binding sfTA," which improve the accuracy of calculating binding correlation energies in low-dimensional bilayer materials by incorporating binding interactions into the twist-angle selection process.
The paper introduces , an open-source physics-informed neural network architecture that learns the continuous mapping between physical parameters and their corresponding spectra, demonstrating high-precision performance by accurately predicting the quasinormal modes of Kerr black holes across a wide range of spins.
The paper demonstrates that while embedding a fractal geometric prior into a machine learning model improves the prediction of subgrid interfacial area density in multiphase flows, the effectiveness of this physics-based inductive bias is regime-dependent, performing well in corrugation-dominated flows but failing during topology-changing fragmentation.
The paper proposes the Synchronized Molecular Dynamics (SMD) method, a multiscale computational framework that efficiently simulates thin-layer flows of complex fluids by coupling sparse local molecular dynamics simulations with a macroscopic lubrication description through iterative synchronization of conservation laws.
This paper presents an extended direct-forcing immersed boundary method (DF-IBM) that couples Navier-Stokes equations with Newton-Euler equations through an implicit, computationally efficient algorithm to stably simulate free-moving fluid-rigid body interactions across various density ratios.
This paper introduces a gradient-free optimization heuristic for discrete quantum optimal control that uses Matrix Product State (MPS/TT) sampling to iteratively refine a search distribution toward high-performing control sequences.
This paper introduces a generalized family of boundary injection rules for collisionless systems that allows for the analytical derivation of stationary distribution functions, demonstrating that varying the boundary velocity distributions can drive the system into non-thermal states characterized by non-trivial spatial density and temperature profiles.