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Found 7 papers, 1 papers with code
SymbolicGPT: A Generative Transformer Model for Symbolic Regression
Symbolic regression is the task of identifying a mathematical expression that best fits a provided dataset of input and output values.
Symbolically Solving Partial Differential Equations using Deep Learning
We describe a neural-based method for generating exact or approximate solutions to differential equations in the form of mathematical expressions.
A Neuro-Symbolic Method for Solving Differential and Functional Equations
When neural networks are used to solve differential equations, they usually produce solutions in the form of black-box functions that are not directly mathematically interpretable.
Logic Guided Genetic Algorithms
Our method, logic-guided genetic algorithm (LGGA), takes as input a set of labelled data points and auxiliary truths (ATs) (mathematical facts known a priori about the unknown function the regressor aims to learn) and outputs a specially generated and curated dataset that can be used with any SR method.
LGML: Logic Guided Machine Learning
We introduce Logic Guided Machine Learning (LGML), a novel approach that symbiotically combines machine learning (ML) and logic solvers with the goal of learning mathematical functions from data.
Discovering Symmetry Invariants and Conserved Quantities by Interpreting Siamese Neural Networks
It turns out that in the process of learning which datapoints belong to the same event or field configuration, these SNNs also learn the relevant symmetry invariants and conserved quantities.
Classification-based RNN machine translation using GRUs
We report the results of our classification-based machine translation model, built upon the framework of a recurrent neural network using gated recurrent units.
