1205 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 hybrid digital-thermodynamic algorithm that significantly accelerates thermodynamic computing by using a classical processor to compute optimized initial states—inspired by the Mpemba effect—that suppress slow relaxation modes, thereby reducing the thermalization time required for matrix operations like inversion and determinant calculation.
This paper introduces "Quantum Neural Physics," a hybrid quantum-classical framework that maps discretized partial differential equations into parameter-free quantum convolutional kernels with logarithmic circuit depth, enabling efficient and accurate solutions for complex physical problems like the Navier-Stokes equations on quantum simulators.
This paper demonstrates that reconfigurable topological valley-Hall interfaces in two-dimensional periodic metamaterials can be created and relocated within the same geometric structure solely by switching the boundary conditions (Dirichlet or Neumann) of cylindrical inclusions, a phenomenon analyzed through a unified matched-asymptotic framework that yields both Floquet-Bloch spectra and Foldy multiple-scattering systems.
This paper presents an iterative algorithm that utilizes Fourier and Abel transforms along with real-space constraints to successfully restore missing low scattering angle data in anisotropic two-dimensional diffraction patterns of isolated molecules, requiring only approximate knowledge of the molecule's internuclear distances.
This paper presents a highly accurate and scalable Graph Neural Network potential for aluminum, developed through a sequential-refinement workflow, which enables million-atom simulations to reveal how stacking-fault energetics and diffusion critically influence solidification microstructures and mechanical properties, outperforming both classical and general-purpose machine learning potentials.
This paper introduces soliton_solver, an open-source, GPU-accelerated Python package that utilizes a modular, theory-agnostic architecture to efficiently simulate and visualize topological solitons across diverse two-dimensional non-linear field theories.
This paper presents a data-driven LSTM surrogate model capable of learning nonlinear mappings from wave-vessel time series to accurately reproduce both parametric roll episodes and their associated statistical shifts, utilizing training data from either physical experiments or high-fidelity simulations to enhance operability and risk assessment.
This paper demonstrates a novel method for reconstructing the 3D electron density of tumbling proteins by utilizing ion signatures from Coulomb explosions to determine molecular orientations, achieving angular accuracy and resolution comparable to or better than conventional diffraction-only techniques in single-particle X-ray imaging.
This study establishes two power law relations for the thermal slip length at normal liquid/solid interfaces by analyzing the in-plane structure and vibrational frequency of the contact zone in Lennard-Jones systems, revealing that enhanced translational order and frequency matching significantly reduce thermal impedance.
This paper proposes Discontinuity-aware Physics-Informed Neural Networks (DPINNs), a novel framework that integrates adaptive Fourier-feature embeddings, a discontinuity-aware Kolmogorov-based architecture, mesh transformation, and learnable artificial viscosity to overcome spectral bias and instability, thereby achieving superior accuracy in solving partial differential equations with sharp discontinuities and complex geometries.