arXiv AI
Artificial Intelligence in Mechanical Engineering: Foundations, Opportunities, Challenges, and Responsibilities
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Atoms to Processes: The Role of Artificial Intelligence and Machine Learning in Chemical Engineering
The article discusses how the rapid development of artificial intelligence (AI) and machine learning (ML) is transforming chemical engineering by influencing problem formulation, analysis, and solution across a wide range of applications, from atomic-scale simulations to industrial operations. It highlights recent methodological advances and representative uses, noting a shift from purely black-box models to hybrid and physics-informed frameworks that incorporate conservation laws, thermodynamic consistency, and structural constraints. These integrated approaches enhance robustness, reliability, and human-AI collaboration, ultimately amplifying rather than replacing core chemical engineering principles.
Hugging Face Trending Papers
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PLE: Physical-Engineering's Last Exam
Frontier language models now score above 90% on popular knowledge benchmarks and are increasingly deployed as agents that carry out multi-hour professional work. Yet no existing benchmark tests whethe...
arXiv AI
Jun 12
From Digital to Physical: Digital Agents as Autonomous Coaches for Physical Intelligence
arXiv:2601. 21570v2 Announce Type: replace Abstract: The field of Embodied AI is witnessing a rapid evolution toward general-purpose robotic systems, fueled by high-fidelity simulation and large-scale data collection.
arXiv AI
Aug 17
The Past and Future of AI Scientists
arXiv:2608. 14407v1 Announce Type: new Abstract: We present a survey of the past and future of AI Scientists: machines capable of automating science.
arXiv AI
Jul 28
Physical AI Governance: From Theory to Practice Across Life Cycle
arXiv:2607. 22877v1 Announce Type: new Abstract: With the emergence of Physical AI, artificial intelligence is extending beyond screen-based applications to embodied systems that perceive, interact with, and act in the physical world.
arXiv AI
Sep 23
Human-guided physics-constrained AI agents construct an auditable model of soil-plug evolution
arXiv:2609.23360v1 Announce Type: cross Abstract: Engineering predictions require physical mechanisms to be translated consistently into equations, discretization, code, and validation, yet errors ca...