Data-driven discovery of formulas by symbolic regression

Symbolic Regression-Based Genetic Approximations of the Colebrook Equation for Flow Friction

(2018) Pavel Praks et al.   Water

SISSO: A compressed-sensing method for identifying the best low-dimensional descriptor in an immensity of offered candidates

(2018) Runhai Ouyang et al.   PHYSICAL REVIEW MATERIALS

Study of Li atom diffusion in amorphous Li3PO4 with neural network potential

(2017) Wenwen Li et al.   JOURNAL OF CHEMICAL PHYSICS

Force Field Parametrization of Metal Ions from Statistical Learning Techniques

(2017) Francesco Fracchia et al.   Journal of Chemical Theory and Computation

Learning physical descriptors for materials science by compressed sensing

(2017) Luca M Ghiringhelli et al.   NEW JOURNAL OF PHYSICS

Synchronization control of oscillator networks using symbolic regression

(2017) Julien Gout et al.   NONLINEAR DYNAMICS

Data-driven discovery of partial differential equations

(2017) Samuel H. Rudy et al.   Science Advances

Genetic programming for experimental big data mining: A case study on concrete creep formulation

(2016) Amir H. Gandomi et al.   AUTOMATION IN CONSTRUCTION

Perspective: Machine learning potentials for atomistic simulations

(2016) Jörg Behler   JOURNAL OF CHEMICAL PHYSICS

A novel approach integrating dimensional analysis and neural networks for the detection of localized faults in roller bearings

(2016) I.M. Jamadar et al.   MEASUREMENT

Neural network molecular dynamics simulations of solid–liquid interfaces: water at low-index copper surfaces

(2016) Suresh Kondati Natarajan et al.   PHYSICAL CHEMISTRY CHEMICAL PHYSICS

Machine learning scheme for fast extraction of chemically interpretable interatomic potentials

(2016) Pavel E. Dolgirev et al.   AIP Advances

Constructing high-dimensional neural network potentials: A tutorial review

(2015) Jörg Behler   INTERNATIONAL JOURNAL OF QUANTUM CHEMISTRY

Molecular Dynamics with On-the-Fly Machine Learning of Quantum-Mechanical Forces

(2015) Zhenwei Li et al.   PHYSICAL REVIEW LETTERS

An EPR-based self-learning approach to material modelling

(2013) Asaad Faramarzi et al.   COMPUTERS & STRUCTURES

Inference of hidden variables in systems of differential equations with genetic programming

(2012) Theodore W. Cornforth et al.   Genetic Programming and Evolvable Machines

A new multi-gene genetic programming approach to nonlinear system modeling. Part I: materials and structural engineering problems

(2011) Amir Hossein Gandomi et al.   NEURAL COMPUTING & APPLICATIONS

Grammar-based Genetic Programming: a survey

(2010) Robert I. McKay et al.   Genetic Programming and Evolvable Machines

Efficient hybrid evolutionary optimization of interatomic potential models

(2010) W. Michael Brown et al.   JOURNAL OF CHEMICAL PHYSICS

Intelligent finite element method: An evolutionary approach to constitutive modeling

(2009) A.A. Javadi et al.   ADVANCED ENGINEERING INFORMATICS

Distilling Free-Form Natural Laws from Experimental Data

(2009) Michael Schmidt et al.   SCIENCE