1259 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 review presents the first unified conceptual framework for scientific machine learning in hydrology, organizing fragmented methodological families into a coherent structure to clarify novelty, foster cumulative progress, and guide future research directions.
This paper introduces an exact self-gravity kernel based on modified Bessel functions for thin, hydrostatically supported protoplanetary discs, which overcomes the limitations of traditional smoothing-length approximations by preserving Newtonian features, ensuring computational efficiency, and revealing a previously unnoticed source of gravitational runaway at infinitesimal distances.
This paper presents a three-dimensional grandpotential multi-phase-field model to simulate the solid-state dewetting of polycrystalline thin films, successfully reproducing key phenomenology, establishing new analytical criteria for dewetting onset, and elucidating the critical roles of grain boundaries and triple junctions in the process.
This paper presents a hierarchical bottom-up framework that constructs generalized field-theoretic models for molecular liquids directly from atomistic interactions by mapping to coarse-grained potentials, regularizing short-range divergences, and extending the Hubbard-Stratonovich transformation to arbitrary pair potentials via dual auxiliary fields.
This paper demonstrates that two-fluid simulations employing appropriately chosen Landau-fluid closure parameters can accurately reproduce kinetic-scale energy spectra from fully kinetic Vlasov simulations, even in turbulent regimes far from local thermodynamic equilibrium, thereby validating their use as a computationally efficient alternative for modeling large-scale plasma turbulence.
This paper introduces a hybrid delta-tracking algorithm utilizing a track-length estimator for structured mesh flux tallying, demonstrating its ability to support hybrid-in-energy and hybrid-in-material schemes as well as continuously moving surfaces, while showing significant performance improvements over standard methods in problems with void regions and continuous energy reactor benchmarks.
This paper derives a dynamic Landau-Lifshitz-Bloch-Slonczewski equation set that treats magnetization magnitude as a variable coupled to a thermal bath, thereby accurately modeling heating-induced demagnetization and improving the prediction of critical currents and switching times in high-current spintronic devices where traditional models fail.
This paper introduces stochastic tensor contraction as a highly efficient computational primitive that significantly reduces the cost and scaling of high-order tensor operations in ab initio quantum chemistry, enabling coupled cluster calculations with chemical accuracy at near mean-field costs while outperforming state-of-the-art local correlation approximations.
This paper proposes a Physics Informed Neural Network (PINN) methodology featuring a sequential training algorithm to solve the ill-posed inverse problem of estimating coolant velocity for MOSFET heat sinks, demonstrating theoretical convergence and strong agreement with experimental results.
The paper introduces trainsum, a versatile Python package that leverages the Array API standard and opt_einsum to enable efficient approximation and arithmetic operations on multidimensional quantics tensor trains for applications in simulation, data compression, machine learning, and data analysis.