1258 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 investigates the one-dimensional non-reciprocal Cahn-Hilliard model, revealing that increasing non-reciprocity drives a transition from defect-laden, disordered states to globally ordered travelling waves under periodic boundary conditions, while inducing fluctuating or partitioned domains under Neumann and Dirichlet conditions.
This study employs coarse-grained molecular dynamics and nodal analysis to establish a mesoscale modeling framework that reveals how amorphous carbon inclusions, CNT geometrical features, and specific morphological traits like high curvature and strong connectivity collectively govern electrical transport in dense carbon nanotube films.
This paper introduces a nonlinear unsteady vortex-lattice vortex-particle method with an adaptive wake conversion strategy that significantly improves computational efficiency and robustness for rotorcraft aerodynamics while maintaining high accuracy across hover, forward flight, and multirotor interaction scenarios.
This paper proposes geometrically nonlinear continuum models of flexomagnetism based on Cosserat micropolar and couple-stress theories, which utilize third-order tensor couplings between micro-dislocation and magnetization to enable centrosymmetric and cubic-symmetric materials with reduced flexomagnetic constants, while deriving governing equations and validating the approach through numerical simulations of a nano-beam.
By integrating machine learning-driven neuroevolution potentials with anharmonic lattice dynamics and the Boltzmann transport equation, this study quantitatively explains the drastic thermal conductivity collapse in single-walled carbon nanotube bundles as a result of symmetry-breaking-induced scattering and new inter-tube scattering channels, while demonstrating the critical necessity of quantum Bose-Einstein statistics to align theoretical predictions with experimental observations.
This paper benchmarks various generative modeling approaches for estimating free energy differences in condensed matter systems, demonstrating that while all yield high accuracy, continuous flows and FEAT offer the best efficiency in energy evaluations while discrete flows provide the lowest inference costs.
This paper proposes a stable differentiable modal synthesis framework that combines scalar auxiliary variable techniques with neural ordinary differential equations to learn nonlinear dynamics, enabling direct physical parameter interpretation and demonstrating success in modeling the nonlinear transverse vibration of a string.
This paper proposes a Hadamard regularization-based separation-of-scales ansatz within the Schwinger-Keldysh formalism to develop a computationally efficient time-stepping algorithm for the Kadanoff-Baym equations, enabling the simulation of damped quantum harmonic oscillators in unstructured environments while capturing non-Markovian and renormalization effects without prohibitive cubic scaling.
This paper introduces an unsupervised "distance learning" framework that uses a neural discriminator to estimate statistical distances between quantum state snapshots, enabling the identification of correlation regimes, reconstruction of phase diagrams, and extraction of critical exponents across diverse many-body systems without relying on traditional representation learning.
This paper introduces TT-Metadynamics, a scalable method that compresses the bias potential in metadynamics into a low-rank tensor train representation using a sketching algorithm, thereby enabling efficient free energy exploration in high-dimensional systems with up to 14 collective variables without the exponential computational cost of standard approaches.