FISolver trains a compact LLM on backward-generated (differential equation, first integral) pairs and uses guided reinforcement learning to outperform larger models and Mathematica on first-integral benchmarks at lower cost.
hub
Deep symbolic regression: Recovering mathe- matical expressions from data via risk-seeking policy gradients
14 Pith papers cite this work. Polarity classification is still indexing.
14
Pith papers citing it
hub tools
citation-role summary
background 2
method 1
citation-polarity summary
verdicts
UNVERDICTED 14representative citing papers
A two-stage symbolic regression plus generative model framework recovers governing interaction terms and forcing in stochastic triad models while accurately predicting statistical moments up to order five.
Neural enhancement replaces selected computational nodes in analytical BRDF models with MLPs identified via hypercube search, yielding accurate, compact models that fit measured reflectance data better than pure analytical ones and integrate with existing graphics pipelines.
Latent Grammar Flow discovers ODEs by placing grammar-based equation representations in a discrete latent space, using a behavioral loss to cluster similar equations, and sampling via a discrete flow model guided by data fit and constraints.
Symbolic rational-function networks recover an admissible PDE from noiseless complete measurements and select the regularization-minimizing parameterization within the architecture.
TSGP uses a pre-trained transformer as a semantic variation operator in genetic programming, generalizes across d-dimensional problems, and outperforms standard GP, SLIM_GSGP, Deep Symbolic Regression, and Denoising Autoencoder GP on 24 datasets while producing more compact solutions.
STRIDE is a self-reflective agent framework that improves accuracy, OOD robustness, and structural recovery in LLM-based symbolic regression by integrating generation, evaluation, repair, and diversity-preserving memory.
GESR uses two BERT models to intelligently direct mutations and crossovers inside genetic programming, yielding higher efficiency and competitive accuracy on symbolic regression benchmarks.
DeRAN converts black-box DRL policies into interpretable symbolic representations for O-RAN automation, retaining 78-87% of original performance while adding built-in transparency.
Expert mathematicians using an AI coding agent for discovery engage in repeated cycles of intentmaking to define goals and sensemaking to interpret outputs.
Programmatic context augmentation lets LLM-based symbolic regression perform code-driven data analysis during search, yielding superior efficiency and accuracy over baselines on LLM-SRBench.
SIGS is a neuro-symbolic framework that discovers analytical solutions to PDEs by generating grammar-constrained expressions, embedding them in a topology-regularised latent manifold, and refining structure and coefficients against the PDE residual and boundary/initial conditions.
Edwin integrates dynamic maximum entropy dimensionality reduction with symbolic regression to recover physically interpretable low-dimensional dynamics from high-dimensional observations that generalize to unseen conditions.
ChatSR aligns scientific data encoders with LLMs to produce formulas that fit data and satisfy explicit priors, reporting SOTA results on 13 symbolic regression benchmarks plus zero-shot handling of unseen prior types.
citing papers explorer
-
Learning First Integrals via Backward-Generated Data and Guided Reinforcement Learning
FISolver trains a compact LLM on backward-generated (differential equation, first integral) pairs and uses guided reinforcement learning to outperform larger models and Mathematica on first-integral benchmarks at lower cost.
-
The finite expression method for turbulent dynamics with high-order moment recovery
A two-stage symbolic regression plus generative model framework recovers governing interaction terms and forcing in stochastic triad models while accurately predicting statistical moments up to order five.
-
Neural Enhancement of Analytical Appearance Models
Neural enhancement replaces selected computational nodes in analytical BRDF models with MLPs identified via hypercube search, yielding accurate, compact models that fit measured reflectance data better than pure analytical ones and integrate with existing graphics pipelines.
-
Neuro-Symbolic ODE Discovery with Latent Grammar Flow
Latent Grammar Flow discovers ODEs by placing grammar-based equation representations in a discrete latent space, using a behavioral loss to cluster similar equations, and sampling via a discrete flow model guided by data fit and constraints.
-
Symbolic recovery of PDEs from measurement data
Symbolic rational-function networks recover an admissible PDE from noiseless complete measurements and select the regularization-minimizing parameterization within the architecture.
-
Transformer Semantic Genetic Programming for d-dimensional Symbolic Regression Problems
TSGP uses a pre-trained transformer as a semantic variation operator in genetic programming, generalizes across d-dimensional problems, and outperforms standard GP, SLIM_GSGP, Deep Symbolic Regression, and Denoising Autoencoder GP on 24 datasets while producing more compact solutions.
-
STRIDE: A Self-Reflective Agent Framework for Reliable Automatic Equation Discovery
STRIDE is a self-reflective agent framework that improves accuracy, OOD robustness, and structural recovery in LLM-based symbolic regression by integrating generation, evaluation, repair, and diversity-preserving memory.
-
GESR: A Genetic Programming-Based Symbolic Regression Method with Gene Editing
GESR uses two BERT models to intelligently direct mutations and crossovers inside genetic programming, yielding higher efficiency and competitive accuracy on symbolic regression benchmarks.
-
Demystifying Deep Reinforcement Learning: A Neuro-Symbolic Framework for Interpretable Open RAN Automation
DeRAN converts black-box DRL policies into interpretable symbolic representations for O-RAN automation, retaining 78-87% of original performance while adding built-in transparency.
-
Intentmaking and Sensemaking: Human Interaction with AI-Guided Mathematical Discovery
Expert mathematicians using an AI coding agent for discovery engage in repeated cycles of intentmaking to define goals and sensemaking to interpret outputs.
-
Programmatic Context Augmentation for LLM-based Symbolic Regression
Programmatic context augmentation lets LLM-based symbolic regression perform code-driven data analysis during search, yielding superior efficiency and accuracy over baselines on LLM-SRBench.
-
Neuro-Symbolic AI for Analytical Solutions of Differential Equations
SIGS is a neuro-symbolic framework that discovers analytical solutions to PDEs by generating grammar-constrained expressions, embedding them in a topology-regularised latent manifold, and refining structure and coefficients against the PDE residual and boundary/initial conditions.
-
Discovering interpretable low-dimensional dynamics using maximum entropy
Edwin integrates dynamic maximum entropy dimensionality reduction with symbolic regression to recover physically interpretable low-dimensional dynamics from high-dimensional observations that generalize to unseen conditions.
-
ChatSR: Multimodal Large Language Models for Scientific Formula Discovery
ChatSR aligns scientific data encoders with LLMs to produce formulas that fit data and satisfy explicit priors, reporting SOTA results on 13 symbolic regression benchmarks plus zero-shot handling of unseen prior types.