1222 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 machine learning-driven inverse-design framework that systematically optimizes the boundary shapes of three-dimensional microwave cavities to maximize electromagnetic helicity, identifying robust, high-performance geometries that surpass traditional heuristic design methods.
This study introduces a Tensor Train representation for high-dimensional unsteady flamelet progress variable manifolds in reacting CFD, demonstrating significant memory reduction and up to 2.4X faster sampling speeds while maintaining combustion fidelity and offering a scalable alternative to machine learning approaches.
SimulCost introduces the first cost-aware benchmark and toolkit for evaluating LLMs in physics simulations, revealing that while multi-round agent interactions improve accuracy over single-round attempts, they remain less efficient and more expensive than traditional parameter scanning methods.
This paper presents a unified machine learning framework that combines machine-learned interatomic potentials with neural classical density functional theory to enable efficient, first-principles multiscale modeling of liquid thermodynamics and phase behavior across homogeneous and inhomogeneous systems.
This paper introduces a Localized Orthogonal Decomposition (LOD) method for the two-dimensional time-dependent nonlinear Schrödinger equation with a wave operator, proving that it preserves conservation laws, ensures unique solutions, and achieves unconditional optimal-order superconvergent error estimates, which are further validated by numerical simulations.
This paper introduces a unified benchmark suite for two-dimensional steady electrohydrodynamic shock-like problems and demonstrates that an LSTM-based Physics-Informed Neural Network (LSTM-PINN) significantly outperforms standard and attention-enhanced architectures by accurately resolving sharp gradients and multiscale structures with minimal computational overhead.
This paper theoretically and numerically distinguishes between conventional anomalous and crystal Hall effects in altermagnetic systems by identifying a previously overlooked hidden C110 rotational symmetry that strictly protects the equivalence of orthogonal conductivity components in collinear configurations.
The paper introduces PICS, a closed-loop neural solver for coupled partial differential equations that guarantees structural admissibility through a partition-of-unity manifold and dynamically optimizes training via entropic tail-risk control to achieve superior accuracy and reliability in multiphysics simulations.
This paper proposes a closed-form conditional diffusion model for data assimilation that leverages kernel density estimation to analytically compute the score function, enabling efficient black-box state estimation for nonlinear, non-Gaussian systems that outperforms traditional ensemble Kalman and particle filters with small to moderate ensemble sizes.
This paper demonstrates that a Fourier neural operator can serve as an accurate and computationally efficient learned forward operator for solving the inverse problem in photoacoustic tomography via gradient-based optimization, outperforming conventional pseudospectral -space methods in efficiency while maintaining high accuracy.