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 introduces HYMOR, an open-source MATLAB and Julia framework that enables global modal, non-modal, and receptivity analyses of high-enthalpy hypersonic flows by employing shock-fitting techniques and real-gas thermochemical models to capture complex physical interactions inaccessible to traditional local methods.
This paper uses a case study of a physics manuscript co-authored with AI to argue that while Large Language Models can handle structural and syntactic tasks, human authors must remain the ultimate "Principal Investigators" responsible for logical rigor and integrity, necessitating the mandatory publication of full AI interaction transcripts to ensure accountability.
This paper introduces a multi-stage, LLM-assisted workflow that uses rigorous LaTeX specifications as intermediate blueprints to successfully generate a scalable Density-Matrix Renormalization Group (DMRG) engine in under 24 hours, achieving a 100% success rate across 16 foundation models and accurately reproducing complex quantum many-body phenomena.
This study demonstrates that optimizing nodal surfaces in fixed-node diffusion Monte Carlo using an antisymmetrized geminal power ansatz significantly improves agreement with CCSD(T) for hydrogen-bonded non-covalent interactions while having negligible effects on dispersion-dominated systems, thereby offering a practical solution to resolve discrepancies in the former and clarifying the nature of errors in the latter.
This paper extends neural-network quantum states to solve strongly interacting few-body problems in continuous space, successfully computing Efimov states and reproducing their key physical features for systems ranging from three to six identical bosons and mass-imbalanced fermions.
This paper introduces HQ-LP-FNO, a hybrid quantum-classical Fourier Neural Operator that utilizes a compact variational quantum circuit mixer to reduce trainable parameters by 15.6% and improve prediction accuracy for three-dimensional laser processing surrogate modeling compared to classical baselines.
This paper introduces an optimization method to generate maximally localized optical modes in multimode fibers—analogue to Wannier functions—which spontaneously organize into concentric rings with complex, non-regular patterns for large mode counts, thereby enabling the design of physically grounded photonic lanterns for arbitrary bundle geometries.
This study demonstrates that heterogeneous swarms combining exploratory and exploitative agents outperform homogeneous groups in locating odor sources within 3-D turbulent environments by effectively mitigating signal spatial correlations, offering new insights for both biological collective behavior and bioinspired engineering algorithms.
This paper investigates the application of low-dimensional semi-supervised latent Bayesian optimization to antimicrobial peptide design, demonstrating that dimensionally-reduced latent spaces enhance interpretability and that strategically organizing latent spaces using both relevant and easily-computable physicochemical properties can improve optimization efficiency.
This paper demonstrates that one-dimensional conservative contact systems possess a hidden linear structure through an exact harmonic oscillator representation, enabling the derivation of a universal damping law and a rigorous lower bound for numerical timesteps.