ViT-K: A Few-Shot Learning Model for Coupled Fluid-Porous Media Flows with Interface Conditions

TL;DR

ViT-K is a few-shot learning model combining Vision Transformers and Koopman operators to efficiently and stably predict coupled fluid-porous media flows from sparse data, enabling real-time multiphysics forecasting.

Contribution

The paper introduces ViT-K, a novel framework that integrates ViT and Koopman operators for stable, accurate long-term predictions of coupled flow systems from limited data.

Findings

ViT-K accurately captures interface physics with high fidelity.

It demonstrates robustness against measurement noise.

It outperforms traditional solvers in inference speed.

Abstract

The numerical simulation of interaction between free flow and porous media, governed by coupled Stokes/Navier--Stokes--Darcy flows, is critical for understanding fluid filtration and physiological transport, yet it is hindered by the high computational cost of resolving interface heterogeneities and the instability of long-term predictions. While deep learning offers surrogate modeling potential, existing frameworks often suffer from exponential error accumulation and poor convergence in multi-physics regimes. To address these limitations, we propose ViT-K, a novel few-shot learning model designed to learn the spatiotemporal evolution of coupled flows from sparse datasets. The ViT-K framework effectively reconstructs the global flow physics on a low-dimensional manifold by combining Vision Transformers (ViT) to capture heterogeneous interfacial features with the Koopman operator to linearize temporal dynamics. By lifting nonlinear dynamics into a globally linear observable space, the ViT-K model provides stability by design, ensuring that prediction errors grow linearly rather than exponentially over time. This theoretical property enables reliable long-term extrapolation even in small-sample regimes. Numerical experiments on benchmark coupled systems demonstrate that ViT-K not only captures complex interface physics with high fidelity but also exhibits exceptional robustness against measurement noise by acting as an implicit spectral filter. The proposed method significantly outperforms traditional solvers in inference speed while maintaining physical consistency, offering a robust paradigm for real-time multiphysics forecasting.