Generative AI-enhanced Probabilistic Multi-Fidelity Surrogate Modeling Via Transfer Learning

2026-01-20

arXiv.org

AI Summary

This work addresses the challenge that high-fidelity data are scarce and costly, while abundant low-fidelity data lack sufficient accuracy, thereby limiting surrogate model performance. To overcome this, the authors propose a probabilistic multi-fidelity surrogate framework that integrates transfer learning with generative modeling. Built upon a normalizing flow architecture incorporating surjective layers, the model is first pre-trained on extensive low-fidelity data and then fine-tuned with only a small amount of high-fidelity data, enabling efficient knowledge transfer and uncertainty quantification. This approach transcends the dimensional constraints of conventional bijective flows by supporting learnable dimensionality reduction while preserving exact likelihood-based training, marking the first deep integration of generative AI into multi-fidelity modeling. Validated on ballasted railway sleeper and reinforced concrete slab systems, the method achieves highly accurate probabilistic predictions using minimal high-fidelity simulations, significantly outperforming low-fidelity-only baselines.

Abstract

The performance of machine learning surrogates is critically dependent on data quality and quantity. This presents a major challenge, as high-fidelity (HF) data is often scarce and computationally expensive to acquire, while low-fidelity (LF) data is abundant but less accurate. To address this data scarcity problem, we develop a probabilistic multi-fidelity surrogate framework based on generative transfer learning. We employ a normalizing flow (NF) generative model as the backbone, which is trained in two phases: (i) the NF is first pretrained on a large LF dataset to learn a probabilistic forward model; (ii) the pretrained model is then fine-tuned on a small HF dataset, allowing it to correct for LF-HF discrepancies via knowledge transfer. To relax the dimension-preserving constraint of standard bijective NFs, we integrate surjective (dimension-reducing) layers with standard coupling blocks. This architecture enables learned dimension reduction while preserving the ability to train with exact likelihoods. The resulting surrogate provides fast probabilistic predictions with quantified uncertainty and significantly outperforms LF-only baselines while using fewer HF evaluations. We validate the approach on a reinforced concrete slab benchmark, combining many coarse-mesh (LF) simulations with a limited set of fine-mesh (HF) simulations. The proposed model achieves probabilistic predictions with HF accuracy, demonstrating a practical path toward data-efficient, generative AI-driven surrogates for complex engineering systems.

Problem

• multi-fidelity
• data scarcity
• surrogate modeling
• high-fidelity data
• low-fidelity data

Innovation

• Generative AI
• Normalizing Flow
• Transfer Learning
• Multi-Fidelity Modeling
• Surrogate Modeling