Unified AI Framework for Scientific Simulation: Multimodal Modeling and Cross-Domain Transfer

Abstract

This paper introduces a unified and extensible framework for scientific simulation and discovery by leveraging the complementary strengths of multimodal representation learning, physics-aware modeling, and task-adaptive architectural components. The proposed system is designed to handle the inherent complexity and diversity of scientific data, integrating structured and unstructured modalities such as graphs, fields, and scalar descriptors. At the core of the architecture lie domain-specific encoders—including graph-based neural networks for molecular structures and continuous field encoders for spatially distributed data—that generate high-fidelity latent representations of physical systems. These embeddings are subsequently processed by physics-informed predictors that incorporate governing equations or learned physical priors to ensure consistency with real-world phenomena. To accommodate a wide variety of scientific tasks, including molecular property prediction, partial differential equation (PDE) simulation, and inverse design, the framework employs flexible output heads with task-specific adaptations, enabling seamless generalization across domains. Extensive experiments conducted on benchmark datasets from computational chemistry, materials science, and fluid dynamics demonstrate the framework’s superior performance in terms of prediction accuracy, out-of-distribution generalization, and physical interpretability. Quantitative metrics confirm improvements over conventional black-box models, while ablation studies validate the contribution of each architectural component. Moreover, qualitative analysis highlights the system’s ability to capture subtle physical patterns and dependencies, making it well-suited for downstream scientific inference and decision-making. Overall, this work presents a significant step toward automating and accelerating scientific discovery, offering a robust and modular platform for neural reasoning across diverse disciplines.