#symbolic regression
6 papers · 1 filter
Can AI Follow In Einstein's Footsteps?
Michael Shalyt, Nathan Regev, Marin SoljaÄiÄ +1
The paper surveys how AI has helped physics discovery, noting a shift from early equation‑finding methods to modern high‑accuracy predictors, and argues that AI still lacks the abi…
Shared Symbolic Backbones for Physically Consistent Multi-Output Symbolic Regression
Manuel Rodriguez
The paper introduces a neuro‑evolutionary symbolic regression approach that discovers a shared symbolic backbone for coupled multi‑output systems, enabling consistent latent expres…
Automatic Ordinary Differential Equations Discovery For Biological Systems Using Large Language Model Powered Agentic System
David Krongauz, Arad Zulti, Eran Segal +1
The paper presents MEDA, an agentic system that combines large language models and symbolic regression to automatically discover ordinary differential equation models for biologica…
Discovering Ordinary Differential Equations with LLM-Based Qualitative and Quantitative Evaluation
Sum Kyun Song, Bong Gyun Shin, Jae Yong Lee
The paper introduces DoLQ, a multi‑agent framework that uses large language models to qualitatively and quantitatively evaluate candidate ordinary differential equations, improving…
Neural Discovery of Memory and Nonlocal Kernels in Integro-Differential Equations with Constrained Kolmogorov--Arnold Networks
Aruzhan Tleubek, Salah A Faroughi
The paper introduces a differentiable solver framework that uses constrained Kolmogorov–Arnold Networks to learn memory and nonlocal kernels of integro-differential equations from…
Neuro-Symbolic ODE Discovery with Latent Grammar Flow
Karin Yu, Eleni Chatzi, Georgios Kissas
The paper presents Latent Grammar Flow, a neuro‑symbolic generative framework that embeds differential equations as grammar‑based tokens in a discrete latent space and uses a discr…