903 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 defines foundational machine-learned interatomic potentials (MLIPs) and articulates six critical open questions that are expected to guide future cutting-edge research in the field.
This paper presents a physics-constrained Gaussian Process framework that leverages Rankine-Hugoniot jump conditions to accurately predict shockwave Hugoniot curves and quantify uncertainties for materials like silicon carbide using a limited number of molecular dynamics simulations.
This paper introduces a generalized Allee-logistic map that bridges continuous and discontinuous extinction transitions, revealing tricriticality with universal scaling relations and demonstrating that the Allee effect suppresses the onset of chaos.
This paper presents a scientific machine learning framework that combines a convolutional neural network surrogate with Bayesian inference to efficiently predict and calibrate multiphase CO2-brine flow dynamics in porous media, demonstrating significant improvements in parameter identification and simulation accuracy over traditional methods using high-resolution "FluidFlower" experimental data.
This paper introduces a tensor-train formulation for the multidimensional inverse Laplace transform that overcomes the curse of dimensionality by reducing computational complexity from exponential to polynomial through low-rank tensor approximations and contractions, demonstrating its efficacy on various multivariate distributions.
This study demonstrates that while Physics-Informed Neural Networks (PINNs) can reconstruct wall shear stress from passive scalar data only when near-wall measurements are available, a differentiable physics framework based on PDE-constrained optimization successfully recovers accurate wall shear stress across diverse measurement scenarios in both canonical and patient-specific cardiovascular flows.
This paper proposes and validates an optimal Langevin Dynamics scheme that significantly reduces sampling bias and improves configurational accuracy for biomolecular simulations, particularly in solvated systems, while maintaining or enhancing computational efficiency and conformational exploration rates.
This paper presents an improved mixing function for a modified upwinding compact scheme that effectively combines the high-order accuracy of compact schemes with the shock-capturing capability of WENO, enabling sharp shock resolution while preserving high resolution in smooth regions for applications like shock-boundary layer and shock-acoustic interactions.
This paper reviews and benchmarks a compressed hybrid BEM/FEM method that transforms dense boundary element matrices into banded dynamic stiffness matrices to efficiently solve elastic wave scattering problems in semi-infinite domains with reduced memory requirements for practical engineering applications.
This paper presents a new multi-resolution hexahedron element formulation based on a novel framework of mutually nesting displacement subspaces, which allows for adjustable resolution levels to modulate structural analysis accuracy more efficiently and rationally than traditional mono-resolution methods.