The expected outcomes of this research include: 1) Proposing a novel learning framework that integrates non-equilibrium thermodynamics with energy-constrained learning, providing innovative solutions for intelligent optimization in complex systems; 2) Validating the performance advantages of this framework under different energy constraints and system dynamics, offering a basis for practical applications; 3) Identifying key technical bottlenecks in machine learning for complex systems and proposing optimization strategies, promoting further development in related fields. These outcomes will help improve the stability and efficiency of AI models in complex systems, advance the deep integration of physics and AI, and provide experimental data and application scenarios for the further optimization of OpenAI models.
Framework
Innovating machine learning with energy constraints and system dynamics.
Innovative Learning Framework for Complex Systems
We analyze and enhance machine learning frameworks using non-equilibrium thermodynamics and energy constraints for improved performance in complex systems through theoretical analysis and experimental validation.