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ORCIDRichard D. Sandberg
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2020 – today
- 2025
[j11]Maximilian Reissmann, Yuan Fang, Andrew S. H. Ooi, Richard D. Sandberg:
Constraining genetic symbolic regression via semantic backpropagation. Genet. Program. Evolvable Mach. 26(1): 12 (2025)- 2024
- [i1]Maximilian Reissmann, Yuan Fang, Andrew S. H. Ooi, Richard D. Sandberg
:
Accelerating evolutionary exploration through language model-based transfer learning. CoRR abs/2406.05166 (2024) - 2022
- [j10]Fabian Waschkowski, Yaomin Zhao, Richard D. Sandberg, Joseph Klewicki:
Multi-objective CFD-driven development of coupled turbulence closure models. J. Comput. Phys. 452: 110922 (2022) - 2021
- [j9]Maximilian Reissmann, Josef Hasslberger, Richard D. Sandberg, Markus Klein:
Application of Gene Expression Programming to a-posteriori LES modeling of a Taylor Green Vortex. J. Comput. Phys. 424: 109859 (2021) - [j8]Raynold Tan, Andrew Ooi, Richard D. Sandberg:
Two Dimensional Analysis of Hybrid Spectral/Finite Difference Schemes for Linearized Compressible Navier-Stokes Equations. J. Sci. Comput. 87(2): 42 (2021) - 2020
- [j7]Yaomin Zhao, Harshal D. Akolekar, Jack Weatheritt, Vittorio Michelassi, Richard D. Sandberg:
RANS turbulence model development using CFD-driven machine learning. J. Comput. Phys. 411: 109413 (2020)
2010 – 2019
- 2019
- [j6]Chitrarth Lav, Richard D. Sandberg, Jimmy Philip:
A framework to develop data-driven turbulence models for flows with organised unsteadiness. J. Comput. Phys. 383: 148-165 (2019) - 2018
- [j5]Matthias Schoepplein, Jack Weatheritt, Richard D. Sandberg, Mohsen Talei, Markus Klein:
Application of an evolutionary algorithm to LES modelling of turbulent transport in premixed flames. J. Comput. Phys. 374: 1166-1179 (2018) - 2017
- [j4]Stefan C. Schlanderer, Gabriel D. Weymouth, Richard D. Sandberg:
The boundary data immersion method for compressible flows with application to aeroacoustics. J. Comput. Phys. 333: 440-461 (2017) - 2016
- [j3]Mark Blackwell, Owen R. Tutty, Eric Rogers, Richard D. Sandberg:
Iterative learning control applied to a non-linear vortex panel model for improved aerodynamic load performance of wind turbines with smart rotors. Int. J. Control 89(1): 55-68 (2016) - [j2]Jack Weatheritt, Richard D. Sandberg:
A novel evolutionary algorithm applied to algebraic modifications of the RANS stress-strain relationship. J. Comput. Phys. 325: 22-37 (2016) - 2015
- [c4]Owen R. Tutty, Mark Blackwell, Eric Rogers, Richard D. Sandberg:
Iterative learning control for load control of smart turbine blades with variable rotation rates. ACC 2015: 3690-3695 - 2014
- [j1]Owen R. Tutty, Mark Blackwell, Eric Rogers, Richard D. Sandberg:
Iterative Learning Control for Improved Aerodynamic Load Performance of Wind Turbines With Smart Rotors. IEEE Trans. Control. Syst. Technol. 22(3): 967-979 (2014) - [c3]Mark Blackwell, Owen R. Tutty, Eric Rogers, Richard D. Sandberg:
Computational fluid dynamics based iterative learning control for smart rotor enabled fatigue load reduction in wind turbines. ACC 2014: 4446-4451 - 2013
- [c2]Owen R. Tutty, Mark Blackwell, Eric Rogers, Richard D. Sandberg:
Iterative learning control of wind turbine smart rotors with pressure sensors. ACC 2013: 5189-5194 - 2012
- [c1]Owen R. Tutty, Mark Blackwell, Eric Rogers, Richard D. Sandberg:
Computational fluid dynamics based iterative learning control of peak loads in wind turbines. CDC 2012: 3948-3953
Coauthor Index
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