ML Anthology

Automated, Interpretable, and Scalable Scientific Machine Learning

Wuyang Chen
AAAI 2025 pp. 28708
doi:10.1609/AAAI.V39I27.35103 /aaai/2025/chen2025aaai-automated/

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.

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Cite

Text

Chen. "Automated, Interpretable, and Scalable Scientific Machine Learning." AAAI Conference on Artificial Intelligence, 2025. doi:10.1609/AAAI.V39I27.35103

Markdown

[Chen. "Automated, Interpretable, and Scalable Scientific Machine Learning." AAAI Conference on Artificial Intelligence, 2025.](https://mlanthology.org/aaai/2025/chen2025aaai-automated/) doi:10.1609/AAAI.V39I27.35103

BibTeX

@inproceedings{chen2025aaai-automated,
  title     = {{Automated, Interpretable, and Scalable Scientific Machine Learning}},
  author    = {Chen, Wuyang},
  booktitle = {AAAI Conference on Artificial Intelligence},
  year      = {2025},
  pages     = {28708},
  doi       = {10.1609/AAAI.V39I27.35103},
  url       = {https://mlanthology.org/aaai/2025/chen2025aaai-automated/}
}