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 "Integral Decimation," a quantum-inspired algorithm that decomposes multidimensional integrals into a spectral tensor train representation to overcome the curse of dimensionality, enabling efficient and accurate calculations of free energy, entropy, and quantum dynamics in high-dimensional systems where conventional methods fail.
The paper introduces \textsc{Dynamite}, a high-performance, reproducible framework that enables the accurate solution of Dynamical Mean-Field Equations up to unprecedented times () by combining adaptive time stepping, non-uniform interpolation, and memory renormalization to overcome previous numerical limitations in studying slow dynamics in complex systems.
This paper demonstrates that using Ensemble Kalman Inversion to systematically calibrate the parameters of a neural network mesoscale eddy parameterization significantly reduces mean state and variability biases in coarse-resolution global ocean models, offering a practical pathway to improve their accuracy without requiring integration to statistical equilibrium.
This study utilizes a 0D Monte Carlo simulation to demonstrate that suprathermal enhancement in advanced fusion fuels is limited, revealing that pure deuterium cannot sustain a chain reaction, DT requires zero neutron leakage for criticality, and aneutronic fuels like BH yield minimal energy gains dominated by neutron-driven processes rather than alpha-particle avalanches.
This study utilizes Discrete Element Method (DEM) simulations to demonstrate that in horizontal stirred bed reactors, increasing rotation speed enhances axial mixing and circulation while higher fill levels slow mixing and reduce axial dispersion, thereby revealing critical trade-offs for optimizing operating conditions in polyolefin production.
This paper introduces a symmetrization scheme that preserves the Mermin-Wagner theorem to resolve pseudo phase transitions in the 2D Hubbard model, applying it within a GW-covariance framework to accurately calculate Green's and spin-spin correlation functions that align with DQMC benchmarks while satisfying fundamental many-body relations.
This paper introduces novel Hyperbolic Shallow Water Moment Equations derived through primitive variable regularization, which analytically overcome the hyperbolicity and accuracy limitations of existing models while preserving the correct momentum equation and enabling interpretable steady states.
This paper compares serial and Monte Carlo batch acquisition functions for Bayesian optimization on synthetic and empirical datasets, concluding that the q-upper confidence bound (qUCB) is the most robust default choice for optimizing unknown black-box functions in up to six dimensions.
This paper reveals a collective Rabi-driven mechanism in which coherent electronic Rabi oscillations within strongly coupled molecular polaritons non-monotonically activate nuclear motion, with maximum efficiency occurring when the polaritonic splitting resonates with a molecular vibrational mode.
This paper proposes a post-hoc phase-steering framework for graph-based CFD surrogates that utilizes sparse autoencoders to extract disentangled latent features and applies temporally coherent, phase-aware rotations to correct phase drift in oscillatory flows without retraining.