1201 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 study develops and validates a reaction-advection-diffusion model to explain non-uniform color distributions and ring-like patterns in paper-based colorimetric sensors, demonstrating that mass transport and reaction dynamics alone can drive spatial variations without evaporation, thereby providing critical insights for optimizing sensor design and protocols.
This paper introduces an adaptive finite volume-particle method (AFVPM) that synergistically combines an Eulerian finite volume approach for bulk flow regions with a Lagrangian smoothed particle hydrodynamics formulation for free surface tracking, utilizing a dynamic conversion strategy and buffer region algorithm to achieve superior accuracy and efficiency in simulating complex free surface flows compared to full SPH methods.
This paper numerically demonstrates that incorporating second-harmonic generation (SHG) as an optical nonlinearity can significantly enhance the classification accuracy and contrast of diffractive neural networks, provided the SHG layers are strategically positioned and experimental constraints are addressed.
This paper presents a framework that combines transferable deep-learning variational Monte Carlo with Gaussian process regression to enable accurate and efficient *ab initio* geometry optimization and potential energy surface exploration for strongly correlated systems, achieving zero-shot chemical accuracy across diverse molecular configurations.
This paper proposes a Physics-Informed Neural Operator (PINO) framework that jointly optimizes learnable dielectric properties and induced current distributions via a hybrid loss function, demonstrating superior accuracy and robustness in solving complex electromagnetic inverse scattering problems across diverse scenarios compared to conventional methods.
This paper extends the tensor-network renormalization group (TNRG) method to incorporate lattice and PT symmetries in two dimensions by applying it to the hard-square lattice gas model, thereby enabling accurate estimation of critical parameters and scaling dimensions for its continuous phase transitions.
This paper presents a general-purpose, machine-learned interatomic potential for CrCoNi alloys based on the neuroevolution potential framework that achieves near first-principles accuracy across the full compositional range, enabling efficient large-scale simulations of complex phenomena like short-range order and composition-dependent mechanical properties.
This study utilizes molecular dynamics simulations to demonstrate that the deflection and heating of a free-standing graphene sheet upon argon nanocluster impact are semi-quantitatively described by linear elasticity theory and the least dissipation principle, respectively.
This paper presents a method for constructing nonconvex quadratic inequalities equivalent to various physics equations under verifiable conditions, enabling the derivation of bounds for physical design problems with quadratic or ratio-of-quadratic objectives.
This paper proposes and demonstrates the feasibility of a statistical model based on a canonical ensemble and the nuclear droplet model for calculating ionisation-cluster size distributions in nanovolumes, specifically addressing scenarios involving low-energy primaries where traditional trajectory models are unsuitable.