1250 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 proposes a scalable machine learning framework for classification and pattern recognition that leverages ensembles of coupled chaotic oscillators, where a neural network automatically learns the necessary coupling terms to induce local resonances for data processing, thereby eliminating the need for expert-designed coupling rules and enabling efficient gradient-based optimization.
This paper introduces an efficient, rejection-free Monte Carlo algorithm that combines Glauber dynamics with hierarchical probabilistic counters to simulate the 2D Random Field Ising Model in time, achieving speedups of over two orders of magnitude compared to the Metropolis method in low-temperature regimes while enabling proper dynamical studies of disordered spin systems.
This paper demonstrates that incorporating temporal memory from earlier time steps into a Quantum Extreme Learning Machine significantly enhances the accuracy of characterizing non-Markovian quantum dynamics, revealing environmental memory effects as a constructive resource for learning.
This paper introduces the Global-Local Hybrid Surrogate (GLHS) algorithm, which combines a global surrogate model with adaptive local surrogates in a buffer zone around the limit state surface to efficiently and accurately estimate rare failure probabilities in high-dimensional complex systems.
This study demonstrates that visibility graphs constructed from Monte Carlo Markov Chain time series of spin models, analyzed through spanning tree counts and random matrix theory, effectively capture continuous phase transitions and complex crossover effects like those in the Blume-Emery-Griffiths model, offering a potential framework for detecting criticality in empirical systems with unknown dynamics.
This paper proposes a data-efficient statistical framework that quantifies discrepancies in molecular dynamics simulations by measuring distances between covariance matrices, enabling the extraction of low-dimensional features that effectively correlate with global physical properties like diffusion coefficients and distinguish between different phases such as ice and liquid water.
This paper introduces a fully local, adaptive repulsive flux within a conservative Allen-Cahn phase-field lattice Boltzmann model to effectively prevent spurious coalescence in multiphase flow simulations by dynamically adjusting interaction strength based on estimated local film thickness, thereby ensuring robust and physically consistent near-contact dynamics without sacrificing computational efficiency.
This paper presents a method to improve the accuracy of decay heat estimates in OpenMC depletion simulations by modifying the simplified CASL chain through the addition of pseudo-nuclides and "delay nuclides" to capture the behavior of missing nuclide groups without sacrificing computational efficiency.
By integrating artificial neural networks with large-scale molecular dynamics simulations, this study demonstrates that the diamond-ice interface significantly alters high-pressure water behavior by lowering the superionic transition temperature, inducing spontaneous bcc-to-fcc phase transitions via the inverse Bain mechanism, and redefining the stability fields of ice phases, thereby resolving discrepancies between theoretical predictions and experimental observations.
This paper introduces ALPHANSO, an open-source Python package that modernizes (,n) neutron source term calculations by incorporating updated nuclear data and offering a modular framework that matches or exceeds the accuracy of legacy tools like SOURCES-4C.