PhysicsNature Communications
Evolving scientific discovery by unifying data and background knowledge with AI Hilbert
R. Cory-wright, C. Cornelio, et al.
AI-Hilbert, a groundbreaking approach by Ryan Cory-Wright, Cristina Cornelio, Sanjeeb Dash, Bachir El Khadir, and Lior Horesh, revolutionizes scientific discovery by integrating AI with experimental data, solving complex polynomial optimization problems, and deriving famous scientific laws like Kepler's Law and Gravitational Wave Power equations. Discover how this innovative method can accelerate our understanding of the universe!
Related Publications
Explore these studies to deepen your understanding
Adjacent work that informs or extends this paper's methodology and findings.
Mathematics
Combining data and theory for derivable scientific discovery with AI-Descartes
C. Cornelio, S. Dash, et al.
Interdisciplinary Studies
ACCELERATING SCIENTIFIC DISCOVERY WITH GENERATIVE KNOWLEDGE EXTRACTION, GRAPH-BASED REPRESENTATION, AND MULTIMODAL INTELLIGENT GRAPH REASONING
M. J. Buehler
Space Sciences
Lunar impact crater identification and age estimation with Chang'E data by deep and transfer learning
C. Yang, H. Zhao, et al.
Physics
Superstrength permanent magnets with iron-based superconductors by data- and researcher-driven process design
A. Yamamoto, S. Tokuta, et al.
