1259 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 a novel, high-order, and conservative discontinuous Galerkin method for solving the relativistic Vlasov–Maxwell system that eliminates Monte-Carlo noise and utilizes a flexible velocity-space mapping to efficiently model diverse extreme high-energy-density plasma phenomena.
This study demonstrates that linearly polarized laser pulses can induce a strong, controllable net magnetization in -wave altermagnets like RuO by driving asymmetric spin dynamics through polarization-selective optical intersite spin transfer and asymmetric spin flips, thereby creating a photo-induced ferrimagnetic state.
This paper proposes a quality-factor inspired deep neural network (QuaDNN) solver that optimizes training data composition, integrates residual connections with channel attention for enhanced feature extraction, and employs a physics-informed loss function to achieve superior accuracy and reduced artifacts in electromagnetic inverse scattering problems.
This paper proposes a physics-driven neural network (PDNN) framework for electromagnetic inverse scattering that iteratively updates solutions by minimizing a loss function combining scattered field constraints and prior information, thereby achieving high-accuracy, stable reconstructions of composite lossy scatterers without relying on large training datasets.
The paper introduces PENCO, a hybrid physics-energy-numerics-consistent neural operator framework that integrates physical laws, energy constraints, and numerical consistency terms into architectures like FNO-4D and MHNO to achieve superior accuracy, stability, and data efficiency in long-term 3D phase-field modeling compared to existing state-of-the-art methods.
This study proposes a novel single-parameter spring-dashpot rolling friction model based on the critical rolling angle to simplify calibration and enhance the computational efficiency of large-scale coarse-grained DEM simulations for non-spherical particles, as validated by its ability to accurately reproduce macroscopic behaviors in industrial incinerator systems.
This paper demonstrates that DeepONet and Fourier Neural Operator (FNO) architectures serve as highly efficient and accurate surrogate models for neutron transport, achieving significant computational speedups over conventional solvers while maintaining precision across various scattering regimes and eigenvalue problems.
This paper proposes a bio-inspired quantum neural network that utilizes Lipkin-Meshkov-Glick (LMG) Hamiltonians and synaptic-efficacy feedback to model neuronal populations as fully connected qubits, demonstrating scalable primitives like stable set points and controllable oscillations for future quantum brain architectures.
This paper compares various evolutionary algorithms for the multi-objective design of nonlinear optical molecules, finding that while NSGA-II excels at optimizing individual objectives, the MOME algorithm achieves superior global diversity and coverage of the solution space.
This paper compares simulated annealing and evolutionary algorithms, both utilizing SMILES string representations, and finds them to be equally effective for discovering molecules with large molecular average hyperpolarizabilities for nonlinear optical applications.