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 investigates an alternative path-finding approach utilizing the A* algorithm to compute the minimal-weight-matching centrosymmetry parameter, offering a potential solution to the flaws identified in existing molecular dynamics analysis methods.
This paper presents a unified gas-kinetic wave-particle (UGKWP) method for simulating multiscale binary-species gas mixtures that accurately captures species-specific velocity and temperature differences across continuum to rarefied regimes by integrating a corrected equilibrium model, Shakhov-based Prandtl number correction, and improved particle transport mechanisms, while demonstrating strong agreement with DSMC results for hypersonic flows.
This study combines first-principles DFT calculations with ultra-high-pressure annealing experiments to demonstrate that silicon diffusion in gallium nitride is extremely limited due to prohibitively high activation barriers, thereby confirming the material's stability for precise doping in advanced electronic applications.
This study demonstrates that machine-learned force fields trained on coupled-cluster data, enhanced by delta-learning and charge-aware approaches to address long-range effects and data limitations, achieve superior accuracy in predicting phonon dispersions and anharmonic vibrational properties for diamond and lithium hydride compared to traditional density functional theory.
Researchers have successfully simulated a 50-qubit universal quantum computer for the first time on Europe's JUPITER exascale supercomputer by leveraging its heterogeneous GH200 architecture through three key innovations: extended memory utilization via CPU-GPU interconnects, adaptive data encoding, and an on-the-fly network traffic optimizer, achieving a 16.6-fold speedup over previous records.
This paper demonstrates that incorporating label projection and a novel embedding network into conditional generative adversarial networks significantly enhances the efficiency and accuracy of inverse designing plasmonic nanostructures from extinction cross-section spectra, achieving order-of-magnitude error reduction and faster convergence across different architectures.
This paper presents a physics-informed convolutional neural network framework that accurately predicts pore-scale velocity fields in complex porous media by integrating physical constraints into the training process, thereby enabling significant acceleration of Lattice-Boltzmann simulations through improved initial conditions.
This paper demonstrates that multimodal large language models can effectively utilize Miller indices as structured latent variables to reason about fracture geometry, reliably inferring plane hypotheses in idealized settings while correctly rejecting such representations when the underlying physics do not support them across diverse material classes.
This paper introduces Lumina, a modular Python-based framework that unifies fragmented multiscale material data for extreme aero-chemo-thermo-mechanical regimes into a centralized, AI-augmented ecosystem to streamline experimental design, validate chemical behaviors, and enhance predictive modeling for advanced defense and aerospace applications.
This paper introduces a wind-relative reinforcement-learning framework for olfactory navigation in turbulent environments, demonstrating that an agent using only the time since the last odor detection and a locally estimated wind direction can outperform traditional strategies and adapt its behavior based on wind estimation quality in both mean wind and isotropic turbulence.