1164 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 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 data-driven, operator-based framework that coarse-grains interacting particle systems with clustering dynamics into an efficient, interpretable low-dimensional model, successfully capturing key metastable states and transition pathways through spectral and transition-path analysis.
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.
The paper introduces CATAPULT, a CUDA-accelerated timestepper implemented in the f3d Python package that significantly outperforms existing CPU-based methods for simulating alpha particle confinement in stellarators under both equilibrium magnetic fields and Shear Alfven waves.
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 introduces physics-augmented finite element model updating (paFEMU), a transfer learning framework that leverages sparse regression and multi-modal data to rapidly discover interpretable constitutive models while seamlessly integrating them into existing finite element workflows.
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 proposes a Q-learning model that couples exploration rates with local reputation differences and employs asymmetric, state-dependent reputation updates, demonstrating that this joint mechanism significantly promotes the evolution of cooperation by incentivizing high-reputation agents to exploit known strategies while motivating low-reputation agents to explore new cooperative behaviors.