1190 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 systematic framework utilizing a deep neural surrogate model trained on a physiologically constrained virtual cohort to enable real-time, personalized prediction of blood flow and cardiac output, thereby filtering non-physiological parameters, optimizing synthetic dataset generation, and facilitating the estimation of central aortic hemodynamics from clinical data.
This paper presents a fast and accurate, fully noninvasive framework using physics-informed neural networks (PINNs) combined with a 1-D arterial model to estimate personalized central hemodynamic parameters, such as cardiac output and central systolic blood pressure, from minimal cuff-pressure data in just 5–10 minutes.
The paper presents an efficient algorithm that circumvents the computational infeasibility of enumerating all fine, regular, star triangulations by directly generating Calabi-Yau threefolds that are inequivalent under Wall's theorem, thereby reducing the required operations by orders of magnitude.
The paper introduces RiteWeight, an algorithm that estimates stationary distributions from unconverged molecular dynamics data by iteratively reweighting trajectory segments with randomized clustering to eliminate discretization errors and generate accurate observables for both equilibrium and nonequilibrium steady states.
This study systematically compares pre-training/fine-tuning and multi-headed training strategies for machine-learned force fields, revealing that while pre-training offers superior accuracy through method-specific representations, multi-headed training provides a practical trade-off by learning method-independent representations that enable cost-efficient universal models.
This study reveals that Martian brain terrain forms through a two-stage process involving initial patterned ground development driven by liquid water freeze-thaw cycles, followed by vertical sculpting via sublimation in a hyper-arid environment, providing physical evidence for Mars' climatic transition from wet to dry.
This paper proposes a sequential experimental design technique that optimally discriminates between plausible model structures identified by universal differential equations and symbolic regression to efficiently discover missing physics in process systems, demonstrated through its application to a bioreactor.
This paper proposes a novel framework that trains diffusion models using standard data-driven procedures and incorporates physical laws exclusively during the reverse inference stage via PDE residual energy, enabling robust, generalizable, and accurate solutions to various partial differential equations without retraining.
PI-JEPA introduces a label-free pretraining framework for multiphysics reservoir simulation that leverages masked latent prediction on unlabeled parameter fields with operator-split PDE residual regularization, enabling high-accuracy surrogate models with minimal labeled data.
This paper argues that the recent emergence of foundation machine learning interatomic potentials, which combine quantum accuracy with force-field speed without requiring system-specific training, will likely replace density functional theory (DFT) as the primary method in computational chemistry within the next decade.