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 proposes an efficient block-encoding technique using a coordinate transformation and Quantum Signal Processing to implement Linear Combination of Hamiltonian Simulations (LCHS) for simulating linear dissipative differential equations, achieving high success probability and superior efficiency compared to existing methods.
This paper introduces resolution-annealed stochastic gradient ascent (RASTA), a novel Bayesian-based method that successfully determines the atomistic electron density of small proteins from noisy single-molecule X-ray scattering images containing as few as 15 photons per image.
This paper introduces AMBer, an autonomous reinforcement learning framework that efficiently constructs viable neutrino flavor theories by systematically selecting symmetry groups and particle representations while minimizing free parameters, demonstrating its potential to automate complex theoretical model-building tasks.
This paper introduces a rigorous Bayesian framework for MINFLUX microscopy that optimizes scanning patterns to achieve maximal resolution with minimal photons or exposure, potentially reducing the photon count required for 1 nm resolution by a factor of four compared to current heuristic methods.
This paper introduces an open-shell frozen natural orbital approach based on ZAPT2 that significantly reduces the virtual orbital space for quantum eigensolvers while maintaining high accuracy, enabling efficient and precise simulation of singlet-triplet energy gaps in large open-shell systems like the Ir(ppy) complex.
SWEEP is a unified, extensible, and differentiable wave equation solver framework that supports diverse seismic propagation models and plug-and-play components to facilitate advanced gradient-based inversion tasks like full-waveform inversion and least-squares reverse time migration.
This paper introduces "El Agente Forjador," a multi-agent framework that autonomously generates, validates, and reuses computational tools to significantly improve the accuracy, cost-efficiency, and adaptability of AI-driven scientific discovery in quantum simulation compared to static, hand-curated approaches.
This paper presents and analyzes a unified high-order kernel regularization framework that extends to all four Helmholtz boundary integral operators in three dimensions, enabling accurate and efficient solutions to scattering problems through smooth surface integrals without specialized quadrature or singularity handling.
This paper presents a meshless, spectrally accurate numerical method featuring adaptive reparameterization, gauge dynamics, and specialized quadrature schemes to efficiently and precisely simulate the dynamics of axisymmetric vesicles in viscous media.
This paper proposes and validates an improved third-order scheme for pseudopotential lattice Boltzmann models that effectively suppresses spurious velocity oscillations at phase interfaces without adding computational complexity, thereby ensuring more reliable simulations of multiphase flows.