Scheduled presentations:

• Using Description Length to Guide Genetic Programming Improves Symbolic Regression Solutions
  *G. Kronberger (University of Applied Sciences Upper Austria), D. Bartlett (Astrophysics, University of Oxford), H. Desmond (University of Protsmouth), P. Ferreira (Astrophysics, University of Oxford), F. Olivetti de Franca (Federal University of ABC, Santo Andre)
• Identifying tractable probabilistic models through uncertainty-aware symbolic regression
  B. Schuscha (Materials Center Leoben Forschung GmbH), C. Findenig (Materials Center Leoben Forschung GmbH), *M. Mücke (Materials Center Leoben Forschung GmbH), D. Scheiber (Materials Center Leoben Forschung GmbH)
• On the Effect of Prior Specification in Bayesian Symbolic Regression
  *G. Bomarito (NASA Langley Research Center), P. Leser (NASA Langley Research Center), J. Pribe (Analytical Mechanics Associates), G. Weber (NASA Langley Research Center)
• Symbolic discovery of high-dimensional tensorial governing equations from data
  *B. Zhang (Beijing Institute of Technology), J. Lei (Beijing Institute of Technology)
• Data-Driven Discovery of Governing Equations in Fluid Dynamics from Molecular Simulations
  *T. Chen (Beihang University), J. Zhang (Beihang University)