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 Dual Vibration Configuration Interaction (DVCI), a memory-efficient computational program that utilizes a novel Hamiltonian factorization based on duality and second quantization to rapidly and precisely calculate specific infrared vibrational states without constructing large matrix blocks.
This study proposes a macroscopic modeling and optimization framework based on Darcy–Forchheimer theory to design variable-TPMS lattice heat exchangers with non-uniform channel widths, which experimental validation confirms achieve a 28.7% performance improvement over uniform lattice configurations.
This paper proposes a methodology that combines differentiable kinetic simulators with gradient-based optimization to accurately infer plasma collision operators directly from phase space data, demonstrating superior performance and efficiency compared to traditional particle-track-based estimates.
This paper demonstrates that the rotational nature of turbulence inherently teaches neural networks equivariance through implicit data augmentation, and that explicitly enforcing this symmetry as an architectural inductive bias significantly improves generalization across different flow conditions while reducing model complexity.
This study demonstrates through dynamic Monte Carlo simulations that while introducing mobility heterogeneity via different update rates in a two-block 2D Gaussian lattice polymer alters internal relaxation dynamics and block-resolved mean-squared displacement, the center-of-mass diffusion coefficient retains the standard ideal chain scaling of .
This paper introduces Stein Kernelized Molecular Dynamics (SKMD), a novel enhanced sampling method that preserves the Boltzmann distribution while using interacting particle dynamics and symmetry-aware kernels to efficiently acquire diverse, non-redundant training data for the active learning and fine-tuning of machine learning interatomic potentials.
This paper presents a systematic, data-free benchmark of eleven Physics-Informed Neural Network architectures for the stiff Poisson-Nernst-Planck system, demonstrating that the Balanced Residual Decay Rate (BRDR) strategy offers an optimal balance between accuracy and computational efficiency compared to other methods, while providing an open-source implementation for future research.
The paper introduces ATLAS-NN, an adaptive neural network framework that enhances long-time Hamiltonian dynamics modeling by incorporating a learnable temporal scaling mechanism and a two-stage transfer learning strategy, achieving significantly reduced prediction errors compared to standard Hamiltonian Neural Networks and traditional symplectic integrators.
This study utilizes a 2D model of the rat duodenum to demonstrate that intestinal pendular-wave motility is primarily optimized for shearing the mucus barrier rather than bulk fluid pumping, as evidenced by its low pumping efficiency and the finding that viscous energy dissipation is governed by intervillous geometry rather than the dynamic mixing boundary layer.
This paper analyzes the stability of iterative parquet equation solutions by identifying a new source of convergence failure unrelated to vertex divergences and proposes a controlled stabilization strategy that successfully recovers physical solutions in strongly interacting regimes.