Multi-Flow Generalization in Data-Driven Turbulence Modeling: An Exploratory Study

Deep Symbolic Regression for Nonlinear Dynamical Systems

Mini-Batching, Gradient-Clipping, First-versus Second-Order: What Works in Gradient-Based Coefficient Optimisation for Symbolic Regression'

Toward Physically Plausible Data-Driven Models: A Novel Neural Network Approach to Symbolic Regression

Data-Driven Turbulence Modeling: Discovering Turbulence Models using Sparse Symbolic Regression

Data-Driven Turbulence Modelling of Algebraic Reynolds-Stress Models using Deep Symbolic Regression

Bayesian deep learning for system identification

Gene-pool Optimal Mixing in Cartesian Genetic Programming

Evolvability degeneration in multi-objective genetic programming for symbolic regression

Hydraulic modelling of liquid-solid fluidisation in drinking water treatment processes

Symbolic Regression Methods for Reinforcement Learning

Selecting Informative Data Samples for Model Learning Through Symbolic Regression

Data-driven RANS closures for three-dimensional flows around bluff bodies

Multi-objective symbolic regression for physics-aware dynamic modeling

Data­-driven techniques for finding governing equations of noisy nonlinear dynamical systems

Enhancement of data-driven turbulence models for wind turbine wake applications

Inferring algebraic stress models from high-fidelity data and applying machine learning to enhance and generalize turbulence lag models

Constructing parsimonious analytic models for dynamic systems via symbolic regression

Symbolic regression driven by training data and prior knowledge

Symbolic regression of algebraic stress-strain relation for RANS turbulence closure