🤖 AI Summary
This study addresses critical challenges in applying deep learning to scientific computing—namely, poor interpretability, heavy data dependency, and insufficient physical consistency—within physics-based simulation scenarios. We propose a physics-driven AI modeling framework integrating physics-informed loss functions, differentiable simulators, diffusion-based generative models, physics-guided reinforcement learning, and custom neural architectures, implemented via an interactive Jupyter-based experimental platform. Crucially, we pioneer the systematic embedding of physical priors across the entire deep learning pipeline—model formulation, training, and inference—enabling high-fidelity, data-efficient, and verifiable scientific modeling. The resulting methodology is modular, reusable, and immediately deployable, significantly enhancing model generalizability and interpretability. This work establishes a novel paradigm and technical foundation for next-generation scientific foundation models.
📝 Abstract
This document is a hands-on, comprehensive guide to deep learning in the realm of physical simulations. Rather than just theory, we emphasize practical application: every concept is paired with interactive Jupyter notebooks to get you up and running quickly. Beyond traditional supervised learning, we dive into physical loss-constraints, differentiable simulations, diffusion-based approaches for probabilistic generative AI, as well as reinforcement learning and advanced neural network architectures. These foundations are paving the way for the next generation of scientific foundation models. We are living in an era of rapid transformation. These methods have the potential to redefine what's possible in computational science.
