Papers for

computational fluid engineers

Papers whose findings have a practical use for this group, as judged from the abstract. Open a paper to read what it means in practice.

Multi-fidelity particle flow filters improve high-dimensional data analysis

Multi-Fidelity Particle Flow Filters based on Log Homotopy

Abstract: This work proposes a new Multi-Fidelity Particle Flow Filter (MFPFF) based on a linear control variate framework and multilevel Monte Carlo methods. Conventional particle filters often suffer from weight collapse and particle degeneracy in high-dimensional spaces. Particle flow filters address this by smoothly transporting prior particles toward the posterior distribution, thereby avoiding the need for resampling. This work constructs multi-fidelity particle flow filters by augmenting a small number of expensive high-fidelity particles (full-order model runs) with a large number of inexpensive low-resolution particles (reduced-order surrogates constructed via intrusive Galerkin projection). These surrogates act as control variates to enhance the statistical estimate of the full-order posterior, yielding enhanced analysis accuracy at comparable computational cost. The work focuses on two particle flows based on log homotopy, the stochastic Gromov flow and deterministic Exact flow. Numerical experiments with the Korteweg-De Vries (KdV) and Quasi-Geostrophic (QG) test problems demonstrate that the proposed MFPFF achieves accuracy comparable to high-fidelity simulations while significantly reducing computational expense.

Mon 26 OctComputational Engineering, Finance, and Science
The gist
When computers try to guess what’s happening inside complex systems, they often use many random samples called particles. But in very detailed problems, these guesses can become unreliable and slow. The authors created a new method that mixes a few accurate but costly guesses with lots of cheaper, simpler ones to make better predictions faster. They tested their idea on fluid and wave problems and got results just as good as the expensive methods but at much lower cost.
Open → 2610.35712v1

Neural model predicts bubble motion and shape in fluid flows fast

Probabilistic Simulation of Gas-Liquid Interfaces with Coupled Flow Matching on Spherical Harmonics

Abstract: Bubbly flows involve strongly coupled bubble motion, deformation, and hydrodynamic interactions, making high-fidelity simulation computationally expensive. We propose a neural surrogate that jointly predicts bubble trajectories and interface deformation in a compact, shared representation, replacing costly mesh-resolved simulation with fast generative inference. Because two-phase flow dynamics is nonlinear and chaotic, we treat forecasting as a conditional generative problem rather than deterministic regression, predicting entire future windows in a single pass. We train and evaluate the surrogate on BubbleSH, a Front-Tracking dataset of rising deformable bubble swarms, assessing performance using trajectory, shape, and distributional metrics over kinematic, morphological, and interaction-based properties. The surrogate reproduces key flow statistics while running several orders of magnitude faster than the underlying DNS solver.

Mon 12 OctMachine Learning
The gist
Simulating bubbles moving and changing shape in fluids takes a lot of computing power. The authors created a neural network model that can quickly predict how bubbles move and deform together, using a shared representation. Instead of giving just one prediction at a time, their model produces multiple future steps in one go, capturing the complex and chaotic behavior of bubbles. Their approach runs much faster than traditional detailed simulations while still matching key physical properties.
Open → 2610.16327v1