Build on Our Work
If your research relates to dynamic optimization, learning-driven evolutionary computation, transfer optimization, scientific AI, or intelligent simulation, the following resources may help you identify relevant prior work from our group.
Learning-driven Optimization
Enhancing evolutionary algorithms with machine learning to achieve faster convergence, better generalization, and adaptive behavior in complex optimization scenarios.
Methodological Papers
Explores how large language models can serve as intelligent optimizers for dynamic multiobjective problems, leveraging their reasoning and pattern recognition capabilities to guide the evolutionary search.
Addresses the imbalanced distribution problem in transfer learning for dynamic multiobjective optimization by leveraging knee point information to guide knowledge transfer.
Introduces a spatial-temporal knowledge transfer framework that simultaneously exploits spatial correlations among decision variables and temporal patterns across environmental changes for dynamic constrained multiobjective optimization.
Proposes a transfer learning framework that leverages historical optimization knowledge to accelerate convergence in dynamic multiobjective environments.
Scientific AI and Intelligent Simulation
Applying AI to scientific discovery and simulation, with emphasis on differentiable simulation, physics-informed learning, and AI for science.
Methodological Papers
Presents a multimodal AI system that combines visual and textual understanding for comprehensive legal document analysis, demonstrating the potential of AI in specialized domain applications.
BibTeX Collection
A complete BibTeX collection of our papers is available at /publications/bibtex/.