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 two novel algorithms for efficiently sampling two-dimensional isometric tensor network states (isoTNS)—one for independent single-configurations and another for identifying high-probability configurations via greedy search—demonstrating their effectiveness across varying entanglement and system sizes.
The paper introduces **akaitools**, a Python package designed to parse, structure, and analyze unstructured output from AkaiKKR electronic structure calculations, thereby enabling systematic, high-throughput studies of disordered alloys through features like dataclass-based results, visualization tools, and automated input generation.
The paper proposes a classical, dissipative physical model using spherical spins and Langevin dynamics to search an unstructured database with a time complexity of where , claiming to outperform Grover's quantum search algorithm.
This paper proposes a tensor network-based approach for chaotic time series prediction that overcomes the exponential parameter growth of next-generation reservoir computing by decomposing multidimensional arrays, thereby achieving superior accuracy and computational efficiency compared to conventional echo state networks.
This paper introduces the TIME method, a code-independent, multiscale simulation approach that efficiently models 3D Roche lobe overflow, revealing a critical mass transfer rate threshold of approximately $1.01$ that distinguishes between stable, nuclear-timescale evolution and unstable, thermal-timescale accretion.
This paper introduces a rigorous, two-step NPT thermodynamic integration scheme that eliminates the approximate NVT-to-NPT correction required by conventional methods, thereby providing more accurate and direct Gibbs free energy calculations for crystalline solids, particularly those with complex cell-shape fluctuations like CsPbI3.
This paper presents an exact method that exploits mirror symmetries in periodic unit cells to decompose eigenproblems into four independent sub-problems on a quarter-cell, enabling the efficient calculation of stop-band intervals via explicit formulas derived from just three discrete wavevectors without computing the full dispersion diagram.
This paper provides a structured survey of constitutive modelling approaches for magneto-active polymers at finite strains, tracing their evolution from semi-empirical descriptions to advanced thermodynamically consistent frameworks while highlighting current challenges in parameter identification, model validation, and computational implementation.
This study systematically benchmarks the reliability of inverse Physics-Informed Neural Networks for estimating Manning friction in shallow water equations, revealing that while two-dimensional flows allow robust recovery with sparse and noisy data, one-dimensional flows suffer from structural identifiability limitations and that joint depth-velocity observations are essential for accurate parameter identification.
This paper reveals that the "multiple-descent" phenomenon in LSTM training, where performance fluctuates after overtraining, is driven by repeated order-chaos phase transitions, with the model achieving optimal performance at the first critical transition point where the "edge of chaos" is widest, enabling the most effective exploration of weight configurations.