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 utilizes atomistic simulations and machine learning to identify the key material properties—specifically elastic constants and lattice distortion—that govern local slip resistances in refractory multi-principal element alloys, ultimately developing a predictive model for macroscopic yield stress to guide alloy design.
DISCOVER is an open-source, GPU-accelerated symbolic regression framework designed to discover interpretable, physics-motivated mathematical descriptors by allowing users to incorporate domain knowledge and constrain search spaces within modern Python workflows.
This study introduces a Lagged Backward-Compatible Physics-Informed Neural Network (LBC-PINN) that utilizes logarithmic time segmentation and transfer learning to accurately and efficiently simulate and invert long-term one-dimensional unsaturated soil consolidation across multi-scale time domains.
The paper proposes HEDMoL, a method that enhances molecular representation learning by transferring electron-level information from small molecules to large ones, enabling state-of-the-art prediction of molecular physics without the high computational cost of direct electron-level modeling.
This paper provides a systematic evaluation of Deep Material Networks (DMNs) by investigating how various offline training parameters affect online performance and demonstrating that the rotation-free Interaction-based Material Network (IMN) formulation significantly accelerates training without sacrificing accuracy.
This commentary reviews the foundational 2009 paper on Event-Chain Monte Carlo (ECMC), explaining how its shift from detailed balance to global balance has evolved from a specific breakthrough for hard spheres into a generalized, powerful framework for sampling continuous potentials and complex systems.
This paper demonstrates that Ensemble Density Functional Theory (EDFT) is a promising approach for calculating band gaps in periodic systems by showing that, when applied to increasingly large one-dimensional Kronig-Penney models, it provides a reasonable correction to the Kohn-Sham gap in the thermodynamic limit.
This study compares the widely used Borgnakke-Larsen model with the more theoretically rigorous Pullin model for simulating translational-rotational energy exchange in diatomic gases using the DSMC method, demonstrating that the Pullin model provides a more consistent physical foundation while maintaining comparable efficiency to the BL model in highly rarefied flows.
This paper demonstrates that using compressed sensing with overlapping cells can significantly reduce the memory requirements of high-fidelity Monte Carlo neutronic simulations, achieving up to 96.25% memory savings in 3D reconstructions while maintaining high accuracy.
This paper proposes a scalable, field-conserving coarsening operator for parallel octree-based adaptive mesh refinement that uses an projection to prevent systematic drift in conserved quantities during long-horizon simulations of partial differential equations.