922 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 the -Gauss-Logistic map, a new nonlinear dynamical system that exhibits a transition from gradual period-doubling cascades to chaos for to an abrupt onset of chaos without bifurcations for .
This paper presents a novel volume penalization technique that uses a perturbation approach to efficiently and accurately simulate acoustically-actuated flows in complex microchannels by segregating the problem into harmonic first-order and time-averaged second-order systems.
This paper presents a machine-learning-accelerated computational approach that uses surface-specific vibrational spectroscopy to demonstrate how graphene oxidation alters interfacial water structure, providing a quantitative spectroscopic fingerprint to reconcile conflicting experimental data.
This study proposes an active learning approach to improve the prediction of enthalpy of mixing in binary liquids by identifying data gaps and performing targeted *ab initio* molecular dynamics simulations, specifically focusing on refractory elements.
The paper introduces a data-informed quantum-classical dynamics (DIQCD) approach that uses a flexible, time-dependent Lindblad equation to accurately predict the evolution of open quantum systems by optimizing a Hamiltonian to fit sparse and noisy experimental or simulated data.
This paper explores the application of quantum-inspired tensor network methods to enhance the efficiency of classical image processing and optical simulations, such as wave-front propagation and image formation.
This paper develops and benchmarks four machine-learned interatomic potentials (ACE, MACE, GAP, and tabGAP) trained on DFT data to enable accurate, large-scale molecular dynamics simulations of radiation-induced defects and oxygen stoichiometry in YBCO high-temperature superconductors.
This paper proposes a physics-informed framework that couples Kolmogorov-Arnold Networks (KANs) with a level-set formulation to efficiently and accurately solve complex moving boundary problems, such as temperature distribution and interface evolution, without requiring experimental data.
This paper utilizes atomistic simulations and machine learning to identify the key material properties—specifically elastic constants and lattice distortion—that govern local slip resistances in refractory multi-principal element alloys, ultimately developing a predictive model for macroscopic yield stress to guide alloy design.
DISCOVER is an open-source, GPU-accelerated symbolic regression framework designed to discover interpretable, physics-motivated mathematical descriptors by allowing users to incorporate domain knowledge and constrain search spaces within modern Python workflows.