AAAI 2025technical0 citations
Automated, Interpretable, and Scalable Scientific Machine Learning
Abstract
Although Artificial Intelligence (AI) has transformed vision and language modeling, Scientific Machine Learning (SciML) complements data-driven AI via a knowledge-driven approach, enhancing our understanding of the physical world. My work focuses on: 1) automating scientific reasoning with language models, 2) improving geometric interpretation, 3) developing foundation models for multiphysics.
BibTeX
@article{Chen_2025, title={Automated, Interpretable, and Scalable Scientific Machine Learning}, volume={39}, url={https://ojs.aaai.org/index.php/AAAI/article/view/35103}, DOI={10.1609/aaai.v39i27.35103}, abstractNote={Although Artificial Intelligence (AI) has transformed vision and language modeling, Scientific Machine Learning (SciML) complements data-driven AI via a knowledge-driven approach, enhancing our understanding of the physical world. My work focuses on: 1) automating scientific reasoning with language models, 2) improving geometric interpretation, 3) developing foundation models for multiphysics.}, number={27}, journal={Proceedings of the AAAI Conference on Artificial Intelligence}, author={Chen, Wuyang}, year={2025}, month={Apr.}, pages={28708-28708} }