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 paper introduces a reweighting-based estimator method in Path Integral Monte Carlo simulations that enables the efficient calculation of linear, nonlinear, and cross-species density response functions for interacting quantum many-body systems, such as the uniform electron gas, using only unperturbed system samples.
This study demonstrates that implicit velocity correction schemes, specifically linear-implicit and sub-stepping methods, significantly enhance the stability and reduce the overall time-to-solution of scale-resolving simulations for complex high Reynolds number flows by up to a factor of eleven, while maintaining high accuracy even with time steps twenty times larger than explicit limits.
This paper presents a machine learning framework that corrects isotopologue extrapolation errors in molecular energy levels using neural networks for CO and a novel transfer learning approach for CO, thereby significantly improving the accuracy of spectroscopic line lists essential for exoplanet atmospheric studies.
This paper demonstrates that Matrix Product State (MPS) methods can efficiently simulate 2D turbulent Rayleigh-Bénard convection up to Rayleigh numbers of , achieving accurate statistical observables with significantly fewer degrees of freedom than traditional methods and suggesting scalability for investigating the ultimate regime of turbulence.
This paper demonstrates that driving the spin dynamics of strongly coupled radical pairs in cryptochrome through modulated inter-radical distances overcomes sensitivity suppression and significantly enhances geomagnetic field detection via Landau-Zener transitions, suggesting that "live" dynamic magnetoreceptors are more sensitive than static ones.
This paper presents a rigorous Bayesian approach that successfully determines electron densities of small proteins from sparse and noisy single-molecule X-ray scattering images by overcoming limitations such as low photon counts, unknown molecular orientations, and various experimental artifacts.
This paper introduces novel full- and low-rank exponential Euler integrators for the Lindblad equation that unconditionally preserve the physical properties of positivity and trace while offering sharp error estimates and superior performance compared to existing methods.
This paper rigorously proves that the representation complexity of antisymmetric tensor product functions grows exponentially with dimension, demonstrating that low-rank TPFs are fundamentally unsuitable for high-dimensional quantum many-body problems requiring antisymmetry.
This paper proposes an efficient block-encoding technique using a coordinate transformation and Quantum Signal Processing to implement Linear Combination of Hamiltonian Simulations (LCHS) for simulating linear dissipative differential equations, achieving high success probability and superior efficiency compared to existing methods.
This paper introduces resolution-annealed stochastic gradient ascent (RASTA), a novel Bayesian-based method that successfully determines the atomistic electron density of small proteins from noisy single-molecule X-ray scattering images containing as few as 15 photons per image.