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 presents a method to derive the exact elastic continuum model for any discrete spring network based solely on its geometry and topology, enabling the prediction of mechanical properties—including non-affine displacements and auxetic behavior—without the need for computationally expensive simulations.
This study employs machine learning potentials combined with enhanced sampling techniques in molecular dynamics simulations to elucidate the structural evolution and dynamical mechanisms of the ring-to-chain polymerization and reverse processes in liquid sulfur across the -transition, yielding results consistent with experimental observations.
This paper proposes a self-consistent method based on the committor function and a variational principle to efficiently sample and analyze the transition state ensemble, enabling the quantitative ranking of relevant degrees of freedom and the systematic construction of collective variables for rare event transitions without requiring prior input beyond the initial and final states.
This paper proposes "Astral," a novel training loss function for physics-informed neural networks based on error majorants that provides reliable, tight upper bounds on solution errors and superior spatial correlation compared to traditional residual minimization, enabling accurate error estimation and more efficient convergence across diverse partial differential equation problems.
This paper proposes a fully automatic, descriptors-free approach for determining collective variables in enhanced sampling simulations by utilizing geometric graph neural networks to directly process atomic coordinates, thereby ensuring symmetry invariance and physical interpretability across diverse chemical systems.
This paper presents an enhanced sampling method that integrates the iterative variational computation of the committor function with metadynamics-like techniques to achieve balanced and accurate exploration of free energy surfaces, transition states, and competing reactive pathways in rare event simulations.
This study proposes a synergistic framework combining causality-informed training with residual-based adaptive refinement to significantly enhance the accuracy and efficiency of Physics-Informed Neural Networks in solving spatio-temporal PDEs with complex, evolving interfaces, as demonstrated by improved performance in Allen-Cahn phase field modeling.
This paper establishes a theoretical framework for constructing tight error bounds for Physics-Informed Neural Networks (PINNs) approximating Fokker-Planck PDE solutions, demonstrating their accuracy, scalability, and computational efficiency over Monte Carlo methods in solving complex stochastic systems.
This paper proposes a universal reweight-annealing scheme that resolves the general measurement problem in Quantum Monte Carlo simulations by expressing target observables as ratios of partition functions, thereby enabling the calculation of diverse correlations and disorder operators across various quantum models and dimensions while offering broader applications in statistical data analysis.
This paper presents a hybrid surrogate-based multilevel Monte Carlo method that significantly reduces computational costs by up to times while maintaining high accuracy in quantifying uncertainties for the Grad-Shafranov free boundary problem in fusion reactor magnetic equilibrium.