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 purely spatial remedy for order reduction in explicit Runge-Kutta integration of hyperbolic PDEs with time-dependent boundary conditions by redesigning boundary-adjacent derivative operators to satisfy tableau-dependent algebraic conditions, thereby recovering the nominal convergence order without altering the time integrator.
This paper introduces multilevel hybrid transport (MLHT) methods that combine multilevel Monte Carlo techniques with quasidiffusion and second-moment deterministic approaches to efficiently solve the neutral-particle Boltzmann transport equation, demonstrating that variance reduction in correction factors outpaces the increasing computational cost of coarse-grid calculations.
This article introduces a Special Issue on Symbolic Regression for the Physical Sciences, summarizing its conceptual foundations, diverse applications, methodological challenges, and future directions in uncovering interpretable mathematical relationships from data.
This paper presents a generalized probabilistic reparameterization method that enables gradient-based Bayesian optimization for mixed-variable problems with non-equidistant discrete spaces, demonstrating its robustness and efficiency for real-world scientific applications and autonomous laboratories.
This paper introduces a node-driven temporal hypergraph model where Markovian fluctuations in individual node activity generate bursty, non-Poissonian group interaction patterns with long-tailed interevent times and slow autocorrelation decay, offering a simple framework to explain empirical observations of temporal correlations in real-world group dynamics.
This paper advocates for complementing black-box simulation and design tools in nanophotonics with a deeper understanding of the complex roles played by space, time, and additional dimensions.
This perspective argues that differentiable hybrid force fields, which combine physically motivated functional forms with neural network corrections, resolve the critical trade-off between speed, accuracy, and calibratability to enable scalable, closed-loop autonomous discovery of electrolytes.
The paper introduces Tensor-Augmented Convolutional Neural Networks (TACNN), a physically-guided shallow architecture that replaces conventional kernels with generic tensors to capture high-order feature correlations, achieving competitive accuracy on Fashion-MNIST with significantly fewer layers than deep CNNs like VGG-16 and GoogLeNet.
This paper introduces a direction-aware topological data analysis framework that embeds the compression axis into filtration functions to predict Young's modulus in porous materials, demonstrating superior accuracy over traditional direction-agnostic descriptors—particularly for anisotropic structures—while achieving performance comparable to convolutional neural networks with a more compact and transferable representation.
The paper introduces SMC-AI, a scalable algorithmic framework that leverages AI accelerators to achieve the largest reported ML-accelerated atomistic simulation of 4 trillion atoms while decoupling machine learning models from the simulation process to facilitate future integration and portability.