🔬 Category

physics.comp-ph

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.

A Shakhov-based Bhatnagar-Gross-Krook model for polyatomic molecules and for atomic as well as polyatomic mixtures

This paper extends the Shakhov-based Bhatnagar-Gross-Krook (SBGK) model within the PICLas code to simulate polyatomic molecules and their mixtures with non-equilibrium internal degrees of freedom, demonstrating through supersonic and hypersonic test cases that it accurately captures transport properties and shock structures with improved precision over the ESBGK model compared to DSMC results.

Marcel Pfeiffer, Franziska Tuttas2026-04-03🔬 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

Towards Chemically Accurate and Scalable Quantum Simulations on IQM Quantum Hardware: A Quantum-HPC Hybrid Approach

This paper presents a large-scale experimental study on IQM's 24-qubit superconducting processor demonstrating that hybrid quantum-classical approaches, specifically combining Sample-based Quantum Diagonalization with various ansätze and Density Matrix Embedding Theory, can achieve chemically accurate ground-state energies and full potential energy surfaces for molecules ranging from simple benchmarks to pharmacologically relevant systems like amantadine.

Anurag K. S. V., Ashish Kumar Patra, Manas Mukherjee, Alok Shukla, Sai Shankar P., Ruchika Bhat, Radhika T. S. L., Jaiganesh G2026-04-03⚛️ quant-ph

Gradient estimators for parameter inference in discrete stochastic kinetic models

This paper evaluates three machine learning-based gradient estimators (Gumbel-Softmax Straight-Through, Score Function, and Alternative Path) for enabling efficient parameter inference in discrete stochastic kinetic models simulated via the Gillespie algorithm, demonstrating that while the Gumbel-Softmax estimator generally performs well, the other methods offer superior robustness in challenging regimes where variance diverges.

Ludwig Burger, Annalena Kofler, Lukas Heinrich, Ulrich Gerland2026-04-03🔬 physics