Bridging experiments and models, towards a new paradigm : DROP-PINN, a physics-informed neural network to predict droplet rupture in multiphase systems
Overview & Synopsis
A fast and robust PINN-based algorithm is introduced to model turbulent breakage• DROP-PINN infers breakage frequencies directly from image sequences of droplets.• The methodology, based on various simulated configurations, is caseindependent• The DROP-PINN performance is demonstrated by dedicated stirred tank experiments
Indexed from repository: HAL Open Science
(
hal.science)
Keyboard Shortcuts:
← / → for pages • + / - for zoom • 0 to fit width
Powered by Mozilla PDF.js v3.11
Document Information
| Document Title | Bridging experiments and models, towards a new paradigm : DROP-PINN, a physics-informed neural network to predict droplet rupture in multiphase systems |
|---|---|
| Author / Organization | Grégory Bana, Fabrice Lamadie, Sophie Charton, Didier Lucor, Nida Sheibat-Othman |
| Subject & Discipline | Multidisciplinary Academic Open Research |
| Categories | Science & Mathematics |
| Publication Year | 2025 |
| License / Rights | Open Access / Creative Commons |
| Resource Verification | Listed PDF (Verification Pending) |
| Original Host Website |
HAL Open Science
(hal.science)
|
| File Format | application/pdf (Portable Document Format) |
| Language | EN |
| Catalog ID | DOC-202055 |
| Original Source Link | Visit Source Page on hal.science » |