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 presents a hierarchical finite-element framework enhanced by sparse operator-adapted wavelet decomposition that decouples resolution levels to enable efficient, nearly linear-complexity analysis of multiscale electromagnetic problems without requiring the recomputation of coarser-level solutions.
This paper presents and validates a robust recursive regularised lattice Boltzmann method for incompressible magnetohydrodynamics that utilizes a double-distribution formulation and fourth-order Hermite expansion to enhance numerical stability, reduce lattice-dependent artifacts, and eliminate the need for explicit velocity gradient calculations.
This paper demonstrates that utilizing temperature profiles derived from high-fidelity Multiphysics simulations, rather than uniform profiles, to construct Fission Matrix databases significantly improves the accuracy of multiplication factor and fission source distribution predictions for Molten Salt Fast reactors.
Inspired by the Hohenberg-Kohn theorem, this paper proposes an operator-centered machine learning framework that uses the external nuclear potential as input to construct hierarchical representations for efficiently modeling molecular properties and learning operator-to-operator maps, such as those for Fock and density matrices.
This paper introduces a novel particle-based virtual ultrasound machine utilizing a specialized smoothed dissipative particle dynamics variant with implicit pressure solving and negative-pressure stabilization to efficiently simulate acoustic wave-matter interactions across MHz-GHz frequencies, demonstrated through the modeling of microbubble acoustophoresis for drug delivery applications.
This paper presents a novel Bayesian inference method using a machine-learning surrogate model trained on GPU-accelerated simulations to accurately determine the spatially varying impurity density of high-purity germanium detectors from capacitance measurements, overcoming the limitations of traditional manufacturer data.
This paper introduces the Resolution Independent Neural Operator (RINO), a framework that extends DeepONet to handle input and output functions discretized at arbitrary sensor locations by employing dictionary learning with implicit neural representations (specifically SIRENs) to project data into a compatible coefficient space.
This paper proposes an efficient ILUES-based adaptive Gaussian process method that iteratively constructs a surrogate for the unnormalized posterior density and employs a Gaussian mixture MCMC sampler to accurately solve multimodal Bayesian inverse problems with limited forward model evaluations.
This study proposes an unsupervised, physics-informed neural network approach that integrates train load data and bridge response with governing differential equations to effectively identify, localize, and quantify damage in steel truss railroad bridges while incorporating prior inspection knowledge.
This paper introduces a stochastic cluster expansion framework that enables near-DMRG accuracy for total correlation energies in large condensed-phase systems by combining exactly treated subspaces with randomly sampled environment orbitals, thereby eliminating the need for prior active space selection and facilitating high-accuracy studies of chemical processes in complex environments.