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 presents a surrogate-accelerated hierarchical Bayesian calibration framework that successfully derives data-informed dissipative particle dynamics models for commercial ultrasound contrast agents by inferring their mechanical parameters from force spectroscopy data across multiple bubble diameters.
The paper introduces NEPMaker, a D-optimality-driven active learning framework integrated with the GPUMD package that enables the efficient construction of robust and transferable neuroevolution potentials for large-scale simulations of complex materials by embedding extrapolative atomic environments into locally periodic structures to minimize labeling costs.
This paper introduces MolCryst-MLIPs, an open database featuring fine-tuned MACE machine-learned interatomic potentials for nine molecular crystal systems, developed via an automated pipeline to enable reliable production molecular dynamics simulations for studying polymorphism.
This paper proposes the structurally stable boron allotrope - as a pristine spinless topological semimetal that uniquely hosts a symmetry-protected two-dimensional nodal surface alongside multiple types of Weyl fermions, offering an ideal platform to study the interplay of multidimensional topological states.
This paper introduces "distributional inverse homogenization," a noninvasive methodology that leverages macroscopic mechanical property measurements to statistically infer global microstructural information, bridging probability and homogenization theory to solve a challenging class of inverse problems in material science.
This paper presents a novel theoretical framework combining Toeplitz matrix spectral analysis and modal decomposition to demonstrate the weak scalability and wave-number robustness of one-level Schwarz domain decomposition methods for solving time-harmonic Maxwell's equations in waveguides with general cross-sections and various transmission conditions.
This study introduces a Fourier Neural Operator (FNO) based surrogate model that achieves resolution-invariant, rapid, and accurate prediction of multi-grain microstructural evolution, overcoming the computational limitations of traditional phase-field simulations and the generalization issues of existing machine learning approaches.
This paper investigates PDE-constrained optimization for stabilizing Vlasov-Poisson plasma systems, demonstrating that while dispersion analysis yields effective initial guesses, objective functions incorporating time-integrated information are superior for gradient-based optimization due to their more convex-like landscapes.
This paper introduces a computationally efficient body-free simulation framework that successfully reconstructs three-dimensional turbulent cylinder wakes at various Reynolds numbers by prescribing low-dimensional inflow profiles from the absolutely unstable near-wake region, thereby demonstrating that the essential wake dynamics are governed by near-wake instability rather than the explicit presence of the cylinder.
The paper introduces Bayesian-ARGOS, a hybrid framework that combines rapid frequentist screening with focused Bayesian inference to enable automated, statistically rigorous, and computationally efficient discovery of governing equations from noisy, limited data across diverse systems ranging from chaotic dynamics to global climate patterns.