1164 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 Derivative-Constrained PINNs (DC-PINNs), a general framework that enhances Physics-Informed Neural Networks by embedding general nonlinear constraints on states and derivatives via automatic differentiation and self-adaptive loss balancing, thereby stabilizing training and ensuring physically admissible solutions for problems requiring derivative-based relations beyond standard PDE residuals.
This review compares particle-based and continuum modeling paradigms across five fundamental biological systems—the cytoskeleton, membranes, chromatin, biomolecular condensates, and tissues—to guide researchers in selecting appropriate strategies for quantitative biophysical modeling.
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 introduces a nested Fourier-enhanced neural operator (Fourier-MIONet) framework that efficiently and accurately models radiation transfer in 3D fire simulations, achieving global relative errors of 2–4% while significantly reducing computational costs compared to traditional numerical methods.
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 employs variational Monte Carlo with neural quantum states to characterize physical states of 4D Euclidean loop quantum gravity on a complete graph, revealing distinct solution families for the Hamiltonian constraint and its adjoint that correspond to the Ashtekar-Lewandowski and Dittrich-Geiller vacua, respectively, while also providing insights into their relationship with continuum solutions.
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.