1200 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 presents a neural-operator-accelerated concurrent multiscale framework that couples atomistic simulations with continuum finite-element analysis using a Recurrent Neural Operator surrogate to efficiently and accurately model the history-dependent viscoelastic behavior of materials like polyurea at scales previously untractable for direct molecular dynamics coupling.
The paper introduces PRBench, a rigorous benchmark comprising 30 expert-curated physics tasks for evaluating the end-to-end reproduction capabilities of AI agents, revealing that current models struggle significantly with code correctness, data accuracy, and achieving successful reproduction despite their advanced reasoning abilities.
This paper introduces the Spectral Pattern Translator (SPT), a physics-informed deep learning framework that leverages Fourier duality to robustly invert X-ray absorption spectra into transient atomic configurations, thereby overcoming the simulation-to-experiment gap and enabling millisecond-scale autonomous materials discovery.
This paper proposes a finite-precision Lanczos-Golub-Welsch algorithm for constructing probability tables in resonance self-shielding that reformulates Chiba's method as a polynomial-moment problem, yielding lower effective-cross-section errors and avoiding complex responses compared to conventional moment-Pade approaches.
This paper presents an exact analytical phase-space solution for the power-law damped contact oscillator by transforming its nonlinear dynamics into an equivalent linear spring-dashpot system, thereby deriving closed-form expressions for impact characteristics and proving that the coefficient of restitution is independent of initial velocity for all force-law exponents.
This paper presents a data-driven approach that constructs a generalized, anisotropic collision operator for 1D-3V inhomogeneous plasmas by learning directly from molecular dynamics simulations, thereby bridging micro-scale interactions and macroscopic kinetic descriptions while ensuring strict conservation laws and efficient numerical evaluation.
This study demonstrates that short-range atomic ordering in GeSn semiconductor alloys can be precisely quantified using a machine learning-enabled EXAFS analysis and subsequently tuned via annealing to significantly modify the material's bandgap, establishing local atomic order as a critical new degree of freedom for band engineering alongside composition and strain.
The paper introduces Mat3ra-2D, an open-source framework that enables the rapid, reproducible design of realistic two-dimensional materials and interfaces with defects and disorder to address the limitations of AI/ML models trained solely on ideal bulk crystals.
By combining large-scale lattice Boltzmann simulations with an analytical model, this study reveals that fractal trees in urban environments undergo a drag-crisis transition at high Reynolds numbers that is smoothed by structural complexity and shifted to lower speeds by atmospheric turbulence, challenging the assumption that pruning always reduces aerodynamic loading.
This paper demonstrates that the pseudo-fermion method effectively overcomes the fermion sign problem to accurately simulate the energy of strongly quantum-degenerate uniform electron gases, successfully bridging critical parameter regimes where existing methods like RPIMC and CPIMC fail.