1151 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 introduces SPRAY, the first massively parallel GPU-accelerated, mesh-free smoothed particle hydrodynamics code designed to accurately simulate high-intensity laser-plasma interactions by overcoming numerical challenges through tailored SPH formulations, flux-limited diffusion, and a mesh-free WKB-based laser coupling module.
This paper presents the first fully autonomous 6-dimensional beam-tomography system deployed on the LCLS-II DIAG0 line, which utilizes machine learning for adaptive control and generative analysis for rapid reconstruction to enable real-time, high-fidelity monitoring of the photoinjector's phase-space distribution.
This paper extends a machine learning moment closure framework for the radiative transfer equation from one to two dimensions by leveraging the block-tridiagonal structure of the classical model to derive explicit algebraic conditions that guarantee symmetrizable hyperbolicity through a learnable, symmetric positive definite parametrization.
This paper presents a scalable pipeline for constructing an author-centric knowledge graph of battery research using OpenAlex and LLM-enhanced keyphrase extraction to enable cross-institutional expertise tracking, community detection, and semantic similarity analysis beyond traditional citation-based methods.
This study employs machine-learning molecular dynamics to reveal that thermally driven interlayer sliding in ferroelectric MoS₂ moiré superlattices occurs via a domain-wall-mediated, ultralow-barrier collective reconstruction pathway rather than rigid translation, and that minimal sulfur vacancies can trigger a transition from long-range sliding to localized pinning.
This paper introduces a scalable, analytical Hessian-vector product kernel using recursive tangent-state propagation to enable efficient second-order Riemannian trust-region optimization for tensor networks, demonstrating significantly improved convergence and fidelity in quantum circuit compression compared to first-order methods.
Based on first-principles calculations, this study demonstrates that two-dimensional azulenoid-kekulene carbon lattices (AKC-[3,3] and AKC-[6,0]) exhibit a robust second-order topological insulator phase characterized by symmetry-protected fractional corner charges and exotic corner states, establishing them as promising platforms for exploring higher-order topology in carbon allotropes.
This paper introduces an open-source, adaptable tool that applies mechanistic interpretability techniques, such as cosine similarity and PCA, to analyze the internal latent representations of AI weather models like GraphCast, successfully identifying linear combinations of latent channels that correspond to interpretable meteorological features.
This paper introduces an efficient first-order perturbation method with shifting boundaries to accurately predict the statistical distributions of resonance frequencies and quality factors in optical cavities and plasmonic nanoparticles caused by random surface deformations, offering a computationally superior alternative to direct numerical simulations.
This paper proposes a fast, AI-driven Simulation-Based Inference framework using amortized neural posterior estimation to enable real-time, accurate Bayesian condition monitoring of heat exchangers, achieving diagnostic accuracy comparable to traditional MCMC methods while accelerating inference by a factor of 82.