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 investigates how inconsistencies between experimental/numerical data and governing equations create a "consistency barrier" that sets an intrinsic lower bound on the accuracy of Physics-Informed Neural Networks (PINNs).
This paper proposes a novel noise-balanced, multilevel, on-the-fly sparse grid interpolation approach to efficiently create surrogate models for high-fidelity Monte Carlo simulations, overcoming challenges like sampling noise and the curse of dimensionality in multiscale applications such as heterogeneous catalysis.
This paper reformulates Physics-Informed Neural Networks (PINNs) as a statistical learning problem where physics residuals act as indirect data, utilizing singular learning theory and the Local Learning Coefficient to analyze parameter estimation, predictive uncertainty, and extrapolation capabilities for initial and boundary value problems.
This paper proposes a robust, unconditionally stable, and volume-preserving variational front-tracking finite element method for simulating multiphase flows with complex topological features like triple junctions and boundary contacts.
This paper proposes an energetic variational framework to learn the governing laws of dissipative physical systems from either continuous or discrete data by leveraging their underlying energy-dissipation principles.
This study utilizes response theory to evaluate the effectiveness of linear and quadratic approaches in modeling electron charge dynamics and separation, demonstrating that quadratic response theory provides a more accurate description of charge transfer processes than linear response.
This paper introduces a scalable variational method combining matrix product operators and time-dependent variational Monte Carlo to efficiently simulate the non-equilibrium dynamics and steady states of open quantum many-body systems with long-range competing interactions in one and two dimensions.
This first-principles DFT study investigates the thermodynamic, kinetic, optical, and mechanical properties of () hydrides, identifying as the most promising candidate for hydrogen storage due to its superior storage capacity.
UniPhy is a continuous-time neural SPDE solver for planetary-scale weather modeling that integrates Riemannian-Clifford geometry for spatial consistency, non-Hermitian biorthogonal dynamics for open-system thermodynamics, and parallel prefix-sum algorithms for efficient computational integration.
This paper presents a comprehensive phase-field fracture model for coupled thermo-mechanical problems that independently specifies material properties and toughness, successfully unifying various brittle fracture scenarios—such as crack nucleation, propagation, and branching—under diverse thermal shock conditions.