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The convergence of physics and machine learning is revolutionizing scientific discovery, where data and models are becoming integral to understanding complex systems. Deep learning acts as a crucial bridge, translating raw observations into predictive insights across diverse physical phenomena. This structured process involves careful data preprocessing, architecture design, and model optimization, mirroring the evolution of physical thought towards nonlinear understanding. The field grapples with challenges like fuzzy labels and adversarial noise, spurring innovative methods like graph learning and generative networks. Ultimately, this human-driven endeavor aims to enhance our ability to ask better questions and expand our intellectual reach.