🔬 Category

physics.comp-ph

903 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.

Maximizing the magnetic anisotropy of Dy complexes by fine tuning organic ligands: A systematic multireference high-throughput exploration of over 30k molecules

This study employs automated multireference ab initio calculations to systematically screen over 30,000 dysprosium complexes, revealing that fine-tuning organic ligands in pentagonal bipyramidal structures can significantly enhance magnetic anisotropy by up to 100% compared to reference compounds, thereby demonstrating the power of high-throughput computational screening in overcoming chemically non-intuitive design challenges.

Lion Frangoulis, Lorenzo A. Mariano. Vu Ha Anh Nguyen, Zahra Khatibi, Alessandro Lunghi2026-04-06🔬 cond-mat.mtrl-sci

Real-Time Surrogate Modeling for Personalized Blood Flow Prediction and Hemodynamic Analysis

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.

Sokratis J. Anagnostopoulos, George Rovas, Vasiliki Bikia, Theodore G. Papaioannou, Athanase D. Protogerou, Nikolaos Stergiopulos2026-04-06🔬 physics

Fast and Accurate Inverse Blood Flow Modeling from Minimal Cuff-Pressure Data via PINNs

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.

Sokratis J. Anagnostopoulos, Georgios Rovas, Lydia Aslanidou, Vasiliki Bikia, Nikolaos Stergiopulos2026-04-06🔬 physics

RiteWeight: Randomized Iterative Trajectory Reweighting for Steady-State Distributions Without Discretization Error

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.

Sagar Kania, Robert J. Webber, Gideon Simpson, David Aristoff, Daniel M. Zuckerman2026-04-03🔬 physics

Smoluchowski Coagulation Equation and the Evolution of Primordial Black Hole Clusters

This paper presents a comprehensive simulation of primordial black hole (PBH) cluster evolution using the Smoluchowski coagulation equation and Monte Carlo methods to model merger dynamics with and without mass segregation, thereby determining runaway timescales and mass population evolution to explain high-redshift supermassive black holes observed by JWST.

Borui Zhang, Wei-Xiang Feng, Haipeng An2026-04-03🔭 astro-ph

Transformer self-attention encoder-decoder with multimodal deep learning for response time series forecasting and digital twin support in wind structural health monitoring

This paper presents a novel transformer-based encoder-decoder model with multimodal deep learning that accurately forecasts wind-induced structural responses and serves as an adaptive digital twin for the Hardanger Bridge, enabling early detection of structural anomalies without relying on assumptions of environmental or behavioral stationarity.

Feiyu Zhou, Marios Impraimakis2026-04-03🤖 cs.LG