🔬 Category

physics.comp-ph

1258 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.

Covariant Multi-Scale Negative Coupling on Dynamic Riemannian Manifolds: A Geometric Framework for Topological Persistence in Infinite-Dimensional Systems

This paper introduces a geometric framework of Covariant Multi-Scale Negative Coupling on dynamic Riemannian manifolds to counteract dimensional reduction in dissipative PDEs, theoretically proving the finite dimensionality of global attractors while numerically validating the mechanism's ability to stabilize high-dimensional structural complexity against macroscopic dissipation.

Pengyue Hou2026-03-10🔬 physics

Scaling Machine Learning Interatomic Potentials with Mixtures of Experts

This paper introduces Mixture-of-Experts (MoE) and Mixture-of-Linear-Experts (MoLE) architectures for Machine Learning Interatomic Potentials, demonstrating that element-wise routing with shared nonlinear experts achieves state-of-the-art accuracy across multiple benchmarks while revealing chemically interpretable specialization aligned with periodic-table trends.

Yuzhi Liu, Duo Zhang, Anyang Peng, Weinan E, Linfeng Zhang, Han Wang2026-03-10🤖 cs.LG

Percolation on multifractal, scale-free weighted planar stochastic porous lattice

This paper introduces the Weighted Planar Stochastic Porous Lattice (WPSPL), a multifractal, scale-free porous substrate, and demonstrates through analytical and numerical methods that bond percolation on this lattice exhibits a continuous family of distinct universality classes with critical exponents that vary with porosity while satisfying the Rushbrooke inequality.

Proshanto Kumar, Md. Kamrul Hassan2026-03-10🔬 physics

Computationally Efficient Data-Driven Topology Design Independent from High-Infoentropy Initial Dataset

This paper proposes a computationally efficient, sensitivity-free data-driven topology optimization framework that overcomes the limitations of high-information-entropy initialization and expensive simulations by integrating a mesh-independent mutation module and a non-AI rapid identification algorithm to effectively solve strongly nonlinear and non-differentiable engineering design problems.

Jun Yang, Ziliang Wang, Shintaro Yamasaki2026-03-10🔬 physics

Glassy phase transition in immiscible steady-state two-phase flow in porous media

This paper demonstrates that the macroscopic behavior of non-equilibrium two-phase flow in porous media can be successfully predicted by mapping droplet distributions onto an equilibrium spin-glass model derived via machine learning and the maximum entropy principle, revealing that the transition to a glassy flow regime with hysteresis and non-linear dynamics coincides with the spin-glass phase transition.

Santanu Sinha, Humberto Carmona, José S. Andrade Jr., Alex Hansen2026-03-10🔬 physics