917 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 demonstrates that simulation-based inference (SBI) is a viable and potentially superior alternative to traditional empirical tuning for determining neutrino interaction model parameters, as it successfully reproduces and slightly improves upon the MicroBooNE collaboration's tuned GENIE configuration while also approximating the NuWro simulation.
This paper introduces a constant-lag Neural Delay Differential Equations (NDDEs) framework, inspired by the Mori-Zwanzig formalism, to effectively learn non-Markovian dynamics from partially observed data by identifying memory effects through time delays, demonstrating superior performance over existing methods like LSTMs and ANODEs across synthetic, chaotic, and experimental datasets.
This paper extends the Theory of Porous Media to model non-isothermal material injection into porous structures, specifically for percutaneous vertebroplasty, by incorporating local thermal non-equilibrium conditions and demonstrating thermodynamic consistency through numerical simulations.
This paper introduces a hybrid Monte Carlo algorithm for global time-dependent particle transport that utilizes automatic weight windows derived from a second-order accurate, noise-filtered hybrid MC/deterministic solution of low-order second-moment equations to achieve uniform particle distribution and enhanced computational efficiency.
This paper introduces a Physics-Informed Neural Operator (PINO) surrogate model that accelerates the retention analysis of Ferroelectric Vertical NAND devices by over 10,000 times compared to traditional TCAD simulations while maintaining physical accuracy, thereby enabling efficient optimization of device designs against charge detrapping and ferroelectric depolarization.
This paper demonstrates that machine learning models trained to predict the two-electron reduced density matrix (2-RDM) can accurately surrogate correlated wavefunction methods, enabling coupled-cluster-quality electronic structure calculations for large solvated systems at a fraction of the conventional computational cost.
This paper introduces Mixture-of-Experts (MoE) and Mixture-of-Linear-Experts (MoLE) architectures for Machine Learning Interatomic Potentials, demonstrating that element-wise routing with shared nonlinear experts achieves state-of-the-art accuracy across multiple benchmarks while revealing chemically interpretable specialization aligned with periodic-table trends.
This paper introduces the Weighted Planar Stochastic Porous Lattice (WPSPL), a multifractal, scale-free porous substrate, and demonstrates through analytical and numerical methods that bond percolation on this lattice exhibits a continuous family of distinct universality classes with critical exponents that vary with porosity while satisfying the Rushbrooke inequality.
This paper demonstrates that the macroscopic behavior of non-equilibrium two-phase flow in porous media can be successfully predicted by mapping droplet distributions onto an equilibrium spin-glass model derived via machine learning and the maximum entropy principle, revealing that the transition to a glassy flow regime with hysteresis and non-linear dynamics coincides with the spin-glass phase transition.
This paper introduces NATPS, a novel method that combines the time-reversible Mapping Approach to Surface Hopping (MASH) dynamics with transition path sampling to efficiently simulate rare nonadiabatic events and provide mechanistic insights into photochemical processes while significantly reducing computational costs compared to brute-force approaches.