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 introduces FewBodyToolkit.jl, a Julia package based on the Gaussian expansion method that enables the simulation of bound and resonant states in general two- and three-body quantum systems with arbitrary pair-interactions across various spatial dimensions.
This paper demonstrates through phase reduction analysis and numerical simulations that additive and parametric pinning control protocols are equivalent for achieving synchronization in weakly coupled networks of nonlinear oscillators, such as Stuart-Landau systems.
This study identifies that combining extrinsic cohesive-zone models with penalty-based contact in explicit dynamic fragmentation simulations causes severe unphysical energy growth and artificial fragmentation due to stiffness discontinuities and switching errors, ultimately concluding that this approach is unsuitable for long-term, energy-consistent simulations despite the partial mitigation offered by adaptive penalty strategies.
This paper introduces a novel, highly efficient class of Lattice Boltzmann Methods for simulating complex compressible and incompressible magnetohydrodynamic flows, demonstrating near-peak hardware performance and successfully modeling dynamic fluid-structure interactions in a magnetized asteroid scenario.
This paper introduces and validates a semi-explicit Nonsmooth Newmark (NSN) time-integration scheme that robustly handles unilateral contact in dynamic fragmentation simulations by strongly enforcing constraints, thereby achieving superior stability and accuracy over penalty-based methods while revealing that contact dissipation can paradoxically increase fragment counts by improving damage localization.
This paper demonstrates that for a 1D drift-diffusion Poisson benchmark, enforcing a conservative finite volume structure is significantly more critical for achieving stable, long-term autoregressive rollouts with near roundoff error than improving one-step neural regression accuracy or applying learned corrections.
This paper introduces a novel, resource-efficient method for imposing boundary conditions in Quantum Lattice Boltzmann Methods that replaces segmented domain partitioning with a single coherent operation, thereby reducing computational overhead for bounce-back and specular reflection scenarios.
This paper introduces a fully differentiable Particle-Mesh Ewald framework integrated with an E(n)-equivariant Cartesian tensor message passing network to enable end-to-end learning of long-range electrostatics and atomic dipole responses, achieving quantum-accurate forces and scalable O(N log N) performance for condensed-phase and interfacial systems.
This paper proposes a penalty-free quantum optimization approach for lattice protein folding that utilizes a QAOA mixer designed for the maximum independent set problem to avoid quadratic penalties, successfully validating the method via classical simulations for small proteins and extending it to larger systems (up to length ) through a heuristic iterative local-search scheme.
The paper introduces DPA4, a novel SE(3)-equivariant interatomic potential architecture featuring an EMFA SO(2)-equivariant convolution and compiler-friendly training optimizations that achieve state-of-the-art accuracy with significantly reduced parameter counts and training costs, establishing a new accuracy-cost Pareto frontier for large atomistic models.