- The paper presents a novel FieldLVLM framework that integrates domain-specialized models to generate structured textual descriptions from raw scientific field data.
- It uses a two-stage data compression pipeline with VQGAN-RGB to reduce input dimensionality by 99.6%, enabling compatibility with LVLM token limits.
- Parameter-efficient fine-tuning via LoRA achieves high accuracy in tasks like Reynolds number estimation (99.79%) and vortex identification (97.23%).
Enhancing Large Vision-LLMs for Scientific Field Data Interpretation
Introduction
The paper presents FieldLVLM, a framework designed to address the limitations of Large Vision-LLMs (LVLMs) in interpreting scientific field data, particularly in fluid dynamics. While LVLMs have demonstrated strong performance in general vision-language tasks, their efficacy in scientific domains is constrained by the lack of high-quality multimodal datasets and the complexity of field data, which often exceeds model input limits and lacks explicit semantic structure. FieldLVLM introduces a two-pronged approach: a field-aware language generation strategy and a data-compressed multimodal model tuning pipeline, specifically tailored to scientific field data.
Field-Aware Language Generation Strategy
FieldLVLM's first component is a data reformation pipeline that leverages domain-specialized models to extract key physical features from raw field data, such as flow classification, Reynolds number, and vortex patterns. These features are then converted into structured textual descriptions using a LLM, producing consistent and interpretable field language representations. The pipeline is inspired by DeepSeek, which automates text annotation for scientific data, enabling scalable dataset generation without manual labeling.
The strategy integrates high-accuracy models for classification and detection, converting raw velocity-pressure matrices into image representations. Expert-guided categorization distinguishes flow regimes and computes Reynolds numbers, while specialized models detect rare phenomena such as flow instabilities. This hybrid approach combines the precision of domain-special models with the generalization and consistency of LLMs, resulting in high-quality multimodal datasets for downstream learning.
Data-Compressed Multimodal Model Tuning
To address the challenge of high-dimensional field data exceeding LVLM token limits, FieldLVLM implements a two-stage compression pipeline. Raw 256×256 velocity and pressure fields are normalized and mapped to RGB images, which are then encoded into 256 discrete tokens using a pre-trained VQGAN. This reduces input dimensionality by 99.6%, making the data compatible with Qwen2.5-VL's token constraints while preserving critical physical features.
Additionally, representative key values are extracted from the original data to guide model learning, ensuring that essential quantitative features are retained. The textual descriptions generated by the field-aware strategy are also converted into image representations and fed into the image encoder, enriching the semantic structure of the input. The compressed tokens, key values, and semantic images are integrated into the multimodal model, enabling robust scientific reasoning.
Parameter-efficient fine-tuning is performed using LoRA within the LLaMA-Factory framework, updating only LoRA adapters and the multimodal projector while keeping the CLIP-ViT backbone frozen. This reduces trainable parameters by 98.7% compared to full fine-tuning, mitigating catastrophic forgetting and preserving pre-trained visual representations.
Experimental Evaluation
FieldLVLM is evaluated on extended versions of FlowBench and CFDBench, with over 70,000 samples paired with structured language descriptions. Four benchmark tasks are defined: Flow Categorization, Reynolds Number Estimation, Vortex Identification, and Comprehensive Field Analysis. Metrics include accuracy for classification and regression tasks, and cross-entropy loss for semantic consistency.
FieldLVLM achieves 99.79% accuracy in Reynolds Number calculation, 97.23% in Vortex Identification, and 85.41% in Field Data Analysis, outperforming state-of-the-art baselines (DeepSeek-vl-7b-chat, LLaVA-v1.6-vicuna-7b, Llama-3.2-11B-Vision-Instruct), which fail to process scientific field data due to architectural incompatibilities. Ablation studies demonstrate that data compression and key value selection are critical for performance, with compression improving accuracy from 82.28% to 85.41% and key data selection yielding 100% accuracy in field data analysis.
Qualitative results show that FieldLVLM generates structured, domain-specific responses, accurately identifying flow regimes, vortex parameters, and velocity-pressure variations. In contrast, baseline models exhibit generalization errors, non-physical interpretations, and critical parameter misestimations. FieldLVLM's VQGAN-RGB compression mechanism preserves semantic and topological features essential for scientific reasoning.
Implications and Future Directions
FieldLVLM demonstrates that integrating domain-specialized models with LVLMs, combined with effective data compression and semantic enrichment, enables robust interpretation of complex scientific field data. The approach addresses key challenges in dataset scarcity, input dimensionality, and semantic structure, paving the way for broader application of LVLMs in scientific discovery.
Practically, FieldLVLM can be extended to other scientific domains where data is high-dimensional and semantically sparse, such as climate modeling, materials science, and biomedical imaging. The framework's modular design allows for the incorporation of additional domain-specific models and compression techniques, facilitating adaptation to diverse scientific tasks.
Theoretically, the results highlight the importance of hybrid modeling strategies that combine specialized knowledge extraction with general-purpose reasoning. Future research may explore more advanced compression algorithms, automated key value selection, and self-supervised learning approaches to further enhance LVLMs' scientific capabilities. Additionally, expanding benchmark datasets and evaluation metrics will be crucial for assessing model generalization and interpretability in real-world scientific applications.
Conclusion
FieldLVLM introduces a novel framework for improving LVLMs' understanding of scientific field data through field-aware language generation and data-compressed multimodal model tuning. The method achieves superior accuracy across multiple scientific tasks, demonstrating the efficacy of hybrid modeling and compression strategies. This work establishes a foundation for the integration of LVLMs in scientific research and encourages further exploration into robust, generalizable multimodal systems for domain-specific discovery.