1164 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 a purely spatial remedy for order reduction in explicit Runge-Kutta integration of hyperbolic PDEs with time-dependent boundary conditions by redesigning boundary-adjacent derivative operators to satisfy tableau-dependent algebraic conditions, thereby recovering the nominal convergence order without altering the time integrator.
This study utilizes high-Reynolds-number DNS data to demonstrate that eddy viscosity in pressure-driven wall turbulence is configuration-dependent in the outer region, leading to a new Cess-type model with an outer correction function that significantly improves predictions for open-channel flow while maintaining accuracy for closed-channel and pipe flows.
This paper introduces a universal, tree-structured coarse-grained framework enhanced by a Transformer-based propagator that accelerates protein dynamics simulations by 10,000 to 20,000 times while achieving sub-angstrom reconstruction accuracy and generalizing across diverse multi-chain systems.
This paper validates the Shallow Recurrent Decoder network architecture for state estimation on the real-world DYNASTY experimental facility at Politecnico di Milano, demonstrating its ability to accurately reconstruct complex spatio-temporal dynamics of natural circulation systems using only sparse temperature measurements and high-fidelity RELAP5 simulation data.
This study proposes a theoretically designed, experimentally feasible room-temperature multiferroic tunnel junction using a CrSb/In2Se3/Fe3GaTe2 heterostructure that leverages the altermagnetic metal CrSb to achieve high-performance, dual-mode controllable tunneling magnetoresistance and electroresistance for next-generation spintronic applications.
The paper introduces FEMION, a scalable quantum embedding framework that resolves the cost-accuracy dilemma in catalytic metal surface simulations by combining auxiliary-field quantum Monte Carlo with random phase approximation, successfully addressing key challenges in CO adsorption and H2 desorption on Cu(111) while extending the 10-electron-count rule to single-atom catalysis.
This paper presents a framework integrating The Virtual Brain (TVB) and the Collaborative Brain Wave Analysis Pipeline (Cobrawap) to tune a human whole-brain model, demonstrating that the calibrated configuration successfully reproduces biologically realistic spontaneous and evoked dynamics, including complex rhythms and spatio-temporal patterns, which are absent in the default model.
This paper presents a fast 3D nanophotonic inverse design framework based on volume integral equations (VIE) and a tailored adjoint method, which achieves orders-of-magnitude computational speedups over conventional finite-difference methods while successfully optimizing complex devices like power splitters and Bragg gratings.
This study utilizes first-principles calculations to demonstrate that the non-van der Waals material CrSb can host dimension- and facet-dependent altermagnetic and ferromagnetic biferroics and triferroics across various polymorphic phases, offering a new framework for designing multifunctional spintronic devices through structural and surface engineering.
This paper introduces multilevel hybrid transport (MLHT) methods that combine multilevel Monte Carlo techniques with quasidiffusion and second-moment deterministic approaches to efficiently solve the neutral-particle Boltzmann transport equation, demonstrating that variance reduction in correction factors outpaces the increasing computational cost of coarse-grid calculations.