Artificial Intelligence as the Fourth Paradigm of Scientific Discovery: Integrating Physics, Computational Intelligence, and Materials Science for Next-Generation Research and Engineering Subtitle

Engineering notes

Key topics: self-driving, reinforcement learning, foundation model, large language model, prediction. See the paper for implementation details and experimental results.

Chinese explanation / 中文解读

中文解读待补充：本站会优先为端到端自动驾驶、BEV感知、3D目标检测、轨迹预测、路径规划、LiDAR感知等高价值论文补充中文说明。

Original abstract

Alternative Combined Title–1 Why Physics Needs Artificial Intelligence: A Unified Framework for AI-Assisted Scientific Discovery, Computational Physics, and Intelligent Materials Engineering Subtitle From Classical Physics to Autonomous Scientific Intelligence through Machine Learning, Physics-Informed Neural Networks, and Digital Scientific Computing Alternative Combined Title–2 Artificial Intelligence Meets Physics: Transforming Scientific Discovery Through Computational Intelligence, Data-Driven Modeling, and Intelligent Engineering Subtitle Applications in Materials Science, Quantum Physics, Digital Twins, Autonomous Laboratories, and Industrial Innovation Alternative Combined Title–3 The AI Revolution in Physics: A New Scientific Paradigm for Intelligent Discovery and Computational Engineering Subtitle Integrating Machine Learning, Scientific Simulation, Experimental Science, and Physical Laws into a Unified Research Framework Alternative Combined Title–4 Physics Beyond Equations: Artificial Intelligence as the Next Scientific Instrument for Computational Discovery Subtitle A Multidisciplinary Review of AI Applications in Physics, Materials Science, Engineering, and Scientific Research Alternative Combined Title–5 Towards AI-Driven Physics: The Future of Scientific Computing, Intelligent Simulation, and Autonomous Research Subtitle Emerging Perspectives on Physics-Informed Artificial Intelligence and Computational Materials Science Alternative Combined Title–6 Artificial Intelligence for Physics and Materials Science Subtitle A Unified Framework for Scientific Discovery, Digital Engineering, Autonomous Experimentation, and Intelligent Computational Modeling Alternative Combined Title–7 From Newton to Neural Networks Subtitle The Evolution of Physics into the Era of Artificial Intelligence and Autonomous Scientific Discovery Alternative Combined Title–8 Physics in the Age of Artificial Intelligence Subtitle Scientific Computing, Intelligent Materials Design, Digital Twins, and the Future of Engineering Research Alternative Combined Title–9 Intelligent Physics Subtitle Integrating Artificial Intelligence, Machine Learning, Computational Science, and Experimental Research for Next-Generation Scientific Discovery Alternative Combined Title–10 Artificial Intelligence as a Universal Scientific Accelerator Subtitle Applications Across Physics, Materials Science, Engineering, Medicine, Climate Science, and Autonomous Research Detailed Description Abstract-Level Description Artificial Intelligence (AI) is transforming the traditional foundations of scientific research by introducing data-driven intelligence into theoretical, experimental, and computational physics. Modern scientific problems—including quantum systems, multiphysics simulations, climate modeling, fusion energy, materials discovery, nanoscale engineering, and autonomous experimentation—are becoming increasingly complex, often exceeding the capabilities of conventional analytical and numerical techniques. AI provides powerful tools to address these challenges by accelerating computation, discovering hidden relationships in large datasets, optimizing simulations, and enabling autonomous scientific exploration. Inspired by NVIDIA CEO Jensen Huang's observation that "Physics needs AI," this work develops a unified interdisciplinary framework integrating artificial intelligence with classical physics, computational science, materials engineering, and scientific computing. Rather than replacing established physical principles, AI complements them by incorporating machine learning, deep learning, reinforcement learning, generative AI, and Physics-Informed Neural Networks (PINNs) into scientific workflows. The proposed framework demonstrates how AI enhances theoretical modeling, numerical simulation, experimental analysis, digital twins, intelligent manufacturing, and autonomous laboratories. Applications span computational materials science, quantum mechanics, molecular dynamics, finite element analysis, computational fluid dynamics, additive manufacturing, microstructural characterization, inverse materials design, and predictive engineering. The paper also examines emerging challenges including explainability, uncertainty quantification, scientific reproducibility, ethical considerations, computational infrastructure, and the integration of AI with first-principles physics. Finally, it proposes a roadmap toward AI-enabled scientific ecosystems where theory, experiment, simulation, and intelligent learning collaborate to accelerate innovation and technological advancement. Research Theme This research establishes Artificial Intelligence as the Fourth Paradigm of Scientific Discovery, complementing the traditional scientific pillars: Theory Experiment Computational Simulation Artificial Intelligence Together, these four paradigms create a unified scientific ecosystem capable of solving increasingly complex multidisciplinary problems. Scope of the Paper The proposed paper comprehensively covers: Part I – Foundations Evolution of Scientific Discovery Physics in the AI Era Scientific Computing Computational Intelligence Part II – AI Technologies Machine Learning Deep Learning Reinforcement Learning Physics-Informed Neural Networks Generative AI Scientific Foundation Models Explainable AI Part III – Physics Applications Classical Mechanics Quantum Mechanics Statistical Physics Thermodynamics Fluid Dynamics Electromagnetics Astrophysics Nuclear Physics Plasma Physics Part IV – Materials Science Applications Phase Transformations Microstructure Evolution Solidification Grain Growth Alloy Design High-Entropy Alloys Additive Manufacturing Failure Prediction Composite Materials Superconductors Surface Engineering Part V – Engineering Applications Finite Element Analysis Computational Fluid Dynamics Digital Twins Smart Manufacturing Robotics Autonomous Laboratories Predictive Maintenance Intelligent Process Optimization Part VI – Future Scientific Research AI Scientists Autonomous Discovery Scientific Digital Twins Intelligent Research Platforms Quantum AI Scientific Large Language Models Self-Driving Laboratories Novel Contribution This paper proposes a comprehensive conceptual framework in which Artificial Intelligence functions not merely as a computational tool but as a scientific collaborator that augments human reasoning and accelerates discovery while remaining grounded in physical laws. By integrating AI with theory, experimentation, and simulation, it outlines a pathway toward an intelligent scientific ecosystem capable of addressing some of the most challenging problems in physics, materials science, and engineering. Such a perspective makes the work suitable as a high-quality review or perspective article for Scopus-indexed journals in artificial intelligence, computational physics, materials science, and engineering.

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