917 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 a gradient-based optimization method using straight-through Gumbel-Softmax estimation to enable efficient parameter inference and inverse design in exact stochastic kinetic models by approximating gradients through continuous relaxation while preserving discrete stochastic dynamics in the forward pass.
This paper introduces Topology-Aware PINNs (TAPINN), a novel framework that employs supervised metric regularization and alternating optimization to effectively resolve spectral bias and mode collapse in multi-regime physics-informed neural networks, achieving superior convergence stability and accuracy compared to standard and hypernetwork-based baselines.
This paper empirically demonstrates that while Kolmogorov-Arnold Networks (KANs) can compete with MLPs on simple univariate residuals in hard-constrained recurrent physics-informed architectures, they suffer from severe hyperparameter fragility, instability in deeper configurations, and consistent failure on multiplicative terms, ultimately revealing limitations in their additive inductive bias for modeling state coupling in oscillatory systems.
This study presents the first systematic comparison of seven hydrodynamic codes simulating the unstratified streaming instability, revealing broad qualitative agreement across methods while identifying dust modeling choices and resolution as key factors influencing quantitative density statistics and highlighting the superior energy efficiency and scalability of GPU-based implementations.
This study presents an inverse-design framework combining genetic algorithms with frequency-domain micromagnetics to successfully discover unconventional two-dimensional magnonic crystal structures featuring large band gaps, thereby addressing the challenges of optimizing complex lattice geometries for targeted spin-wave properties.
This paper introduces "Ara," an LLM-based agentic workflow that leverages chemical priors to efficiently navigate the design space of covalent organic frameworks, successfully identifying durable and active photocatalysts for solar hydrogen production with significantly higher hit rates and faster convergence than random search or Bayesian optimization.
This paper extends first-principles spin-lattice relaxation theory to include three-phonon processes, demonstrating that while these contributions are negligible for the studied Chromium nitride complex under experimental conditions—thereby validating the weak coupling assumption—the framework reveals that slightly stronger coupling could make three-phonon effects significant at room temperature.
This paper presents a robust and unconditionally stable space-time Galerkin time-domain boundary element method for predicting acoustic scattering and shielding by complex geometries, which is validated through analytical cases and successfully applied to a practical trailing edge-mounted propeller scenario with good agreement to experimental data.
This paper introduces the Open Polymers 2026 (OPoly26) dataset, a publicly released collection of over 6.57 million density functional theory calculations on polymeric systems designed to overcome previous computational limitations and enhance machine learning models for predicting polymer properties.
This paper proposes a novel symmetry-driven generative framework that combines large language models for chemical semantics with a linear-complexity heuristic beam search to rigorously enforce algebraic consistency in Wyckoff patterns, thereby overcoming the combinatorial bottleneck in crystal structure prediction to achieve state-of-the-art performance in discovering new materials without relying on existing databases.