Physics-informed fine-tuning of foundation models for partial differential equations

• URL: http://arxiv.org/abs/2603.15431v1
• Date: Mon, 16 Mar 2026 15:37:03 GMT
• Title: Physics-informed fine-tuning of foundation models for partial differential equations
• Abstract summary: Foundation models for partial differential equations (PDEs) have emerged as powerful surrogates pre-trained on diverse physical systems.<n>We introduce a physics-informed fine-tuning framework that adapts pre-trained PDE foundation models by incorporating physical constraints directly into the fine-tuning objective.<n>Our results demonstrate that physics-informed fine-tuning achieves competitive accuracy without requiring PDE solutions for training.
• Score: 0.0
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Foundation models for partial differential equations (PDEs) have emerged as powerful surrogates pre-trained on diverse physical systems, but adapting them to new downstream tasks remains challenging due to limited task-specific data and distribution shifts. While fine-tuning has proven transformative in natural language processing, best practices for adapting PDE foundation models remain underexplored. Although physics-informed training has successfully trained accurate solvers across a wide range of PDE problems, its potential for fine-tuning data-based foundation models has not been systematically studied. In this work, we introduce a physics-informed fine-tuning framework that adapts pre-trained PDE foundation models by incorporating physical constraints (PDE residuals and boundary conditions) directly into the fine-tuning objective. This enables effective adaptation in data-scarce regimes while promoting physical consistency. We evaluate our method on a downstream task composed of an unseen PDE class and compare it with data-driven finetuning counterparts. Our results demonstrate that physics-informed fine-tuning achieves competitive accuracy without requiring PDE solutions for training. Furthermore, a hybrid fine-tuning strategy yields superior generalization to out-of-distribution scenarios when only minimal training data is available. These findings establish physics-informed fine-tuning as a scalable and data-efficient paradigm, providing a physically interpretable pathway for adapting foundation models in scientific machine learning.

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