3 papers
cs.SC2026
SynPAT: A System for Generating Synthetic Physical Theories with Data
Jonathan Lenchner, Karan Srivastava, Joao Goncalves +2
Machine-assisted methods for discovering physical laws from background theory and data have recently emerged, promising to advance our understanding of the physical world. However,…
cs.AI2025
Bridging the Gap Between Scientific Laws Derived by AI Systems and Canonical Knowledge via Abductive Inference with AI-Noether
Karan Srivastava, Sanjeeb Dash, Ryan Cory-Wright +3
Advances in AI have shown great potential in contributing to the acceleration of scientific discovery. Symbolic regression can fit interpretable models to data, but these models ar…
cs.LG2025
Generative Modeling for Mathematical Discovery
Jordan S. Ellenberg, Cristofero S. Fraser-Taliente, Thomas R. Harvey +2
We present a new implementation of the LLM-driven genetic algorithm {\it funsearch}, whose aim is to generate examples of interest to mathematicians and which has already had some…