Physics-informed neural network-based modeling of the staticreconfiguration of a plate under fluid flow
Bibliographic record
Abstract
This work focuses on the development of a digital twin for the static reconfiguration of a flexible plate subject to a two-dimensional (2D) fluid flow, as a proof of concept for deformable multidomain interfaces. The construction of a digital twin is limited by the scarcity and noisiness of sensors data, as it is an issue for both data-driven and physics-based models. Physics-Informed Neural Networks (PINNs) result from their union and implement physical models in neural networks with additional loss functions. They act as universal function approximators, harnessing automatic differentiation and physical laws to remove the need for a mesh and integrate scarcer data respectively. PINNs have been applied to the dynamic modeling of beams and Vortex-Induced Vibrations (VIV), where the structural reference frame avoids Fluid-Structure Interaction (FSI) deformation, but not to deformable fluidstructure interfaces. Herewith, this work develops PINNs for the FSI of a static reconfiguration of a plate in a 2D fluid flow. Two PINN-based models are proposed: one for the structural formulation of the plate reconfiguration and another for the fluid formulation of a rigid plate in a 2D flow. For the first approach, the boundary conditions and Partial Differential Derivatives (PDE) of a fixed-free Euler-Bernoulli beam, on which only the normal component of the pressure drag is applied, are integrated in the PINN. The results are verified and show good agreement with data from a numerical model, with a Runge-Kutta algorithm to integrate the PDE. The shooting method converts the boundary value problem to an initial value one, and a Mller algorithm iterates from the boundary guess value to the correct one. The fluid formulation is based on the incompressible Navier-Stokes equations with boundary conditions to represent a 2D Poiseuille flow. The rigid plate is implemented with no-slip boundary conditions. The results match the data from a numerical model using the Finite Difference Method (FDM). Both formulations are planned to be combined in a fluid-structure coupled PINN and extended for the dynamic reconfiguration of the plate in a threedimensional flow. For instance, as hydraulic turbines undergo FSI and rotor-stator coupling during their operation, this work could provide a general framework for the development of their digital twins. Yielding the meshless and data-integration properties of PINN, the latter would offer a better reconstruction of their behavior and a more accurate prediction of their maintenance.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".