1191 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.
The paper introduces BuSyNet, a deep learning architecture that integrates dimensional consistency and symplectic geometry to discover interpretable, closed-form symbolic Hamiltonian expressions, achieving superior long-term prediction accuracy and stability on physical systems like the harmonic oscillator and Kepler problem compared to state-of-the-art methods.
This paper identifies and characterizes a previously undocumented class of numerically induced carbuncle-type instabilities in hypersonic bow shock simulations of inert polyatomic gases with low specific heat ratios, highlighting the critical need to distinguish these computational artifacts from genuine physical shock oscillations observed in real thermochemically relaxing flows.
This paper proposes a novel particle merging algorithm for rarefied gas dynamics simulations that conserves arbitrary velocity and spatial moments, as well as collision rates, by solving a non-negative least squares problem, thereby significantly reducing merging-induced errors in macroscopic quantities.
Procela is a novel Python framework that enables mechanistic simulations to dynamically restructure their own architecture at runtime through structural mutation, allowing them to add entirely new mechanisms, remove failing ones, alter resolution policies, and modify the causal graph itself with automatic reversion on failure, thereby achieving significant improvements in accuracy and decision-making in complex scenarios like antimicrobial resistance spread.
This paper demonstrates that neural network emulators of virtual circuits can robustly and effectively control MAST Upgrade plasma shapes in real-time closed-loop simulations, offering a low-latency alternative to traditional physics-based controllers for future fusion devices.
This paper introduces a p-adaptive high-order hybridizable discontinuous Galerkin spectral element method implemented in the open-source PICLas framework to efficiently solve the Poisson equation in electrostatic particle-in-cell simulations by locally refining polynomial degrees in high-gradient regions, thereby significantly reducing the global number of degrees of freedom while effectively modeling complex plasma phenomena.
This paper presents a stable implementation of determinant quantum Monte Carlo simulations at large inverse temperatures by utilizing specific matrix decompositions to overcome numerical instabilities in fermion determinant evaluations and force term calculations, thereby enabling precise simulations at with computational costs scaling as .
This paper reports a proof-of-principle demonstration of the Quantum Approximate Optimization Algorithm (QAOA) for a two-qubit MAX-CUT problem on a room-temperature NV-center quantum processor, successfully reconstructing the cost landscape despite non-projective optical readout limitations.
This paper introduces Equitrain, a LoRA-based fine-tuning framework that significantly enhances the accuracy of machine-learning interatomic potentials for predicting phonon and thermal properties across diverse materials using minimal additional training data, outperforming both pretrained and scratch-trained models.
This paper introduces Simulated Bifurcation Quantum Annealing (SBQA), a quantum-inspired optimization algorithm that incorporates inter-replica interactions to mimic quantum tunneling, demonstrating superior performance over the standard Simulated Bifurcation Method on sparse and rugged energy landscapes while maintaining efficiency and versatility across diverse problem families.