Computational Geosciences publishes high quality papers on mathematical modeling, simulation, numerical analysis, and other computational aspects of the geosciences. In particular the journal is focused on advanced numerical methods for the simulation of subsurface flow and transport, and associated aspects such as discretization, gridding, upscaling, optimization, data assimilation, uncertainty assessment, and high performance parallel and grid computing.
Papers treating similar topics but with applications to other fields in the geosciences, such as geomechanics, geophysics, oceanography, or meteorology, will also be considered.
The journal provides a platform for interaction and multidisciplinary collaboration among diverse scientific groups, from both academia and industry, which share an interest in developing mathematical models and efficient algorithms for solving them, such as mathematicians, engineers, chemists, physicists, and geoscientists.

Editors-in-Chief:
Clint N. Dawson
University of Texas at Austin, TX, USA
Mary F. Wheeler
University of Texas at Austin, TX, USA
Ivan Yotov,
University of Pittsburgh, PA, USA
Founding Managing Editor:
J.-C. van Duijn
Associate Editors:
M.T. Balhoff, The University of Texas at Austin, TX, USA
Pore scale modeling; multiscale modeling
J. Caers, Stanford University, CA, USA
Data science, uncertainty quantification
A.H. Elsheikh, Heriot-Watt University, Edinburgh, UK
Inverse modeling
E. Gildin, Texas A & M University, College Station, TX, USA
Data assimilation; reduced order methods
I. Hoteit, King Abdullah University of Science and Technology, Thuwal, Saudi Arabia
Data assimilation and parameter estimation
M. Iskandarani, University of Miami, FL, USA 
Numerical methods for ocean hydrodynamics
J. Ita, Shell Global Solutions US Inc., Katy, TX, USA
Geomechanics and multi-scale modeling 
B. Jafarpour, University of Southern California, Los Angeles, CA, USA
Stochastic Descriptions, Inverse Modeling, Optimization for Subsurface Flow, Transport Systems
H. Klie, DeepCast.ai, Houston, TX, USA
Data sciences for solving subsurface problems
P. Knabner, Universität Erlangen, Germany 
Modeling, analysis, numerical approximation of reactive transport process in porous media 
M.A. Murad, LNCC-CMC, Rio de Janeiro, Brazil 
Geomechanics
I.S. Pop, Hasselt University, Belgium
Analysis and modeling of flow through porous media
A.C. Reynolds, University of Tulsa, OK, USA
Data assimilation, reservoir optimisation, uncertainty quantification
S. Sun, King Abdullah University of Science and Technology, Thuwal, Saudi Arabia
Numerical methods for flow and transport
D.M. Tartakovsky, Stanford University, CA, USA
Uncertainty quantification, multiscale modeling, data-driven simulation
A. Tompson, Lawrence Livermore National Laboratory, CA, USA 
Hydrologic flow
T. van Leeuwen, Utrecht University, The Netherlands
Computational methods for inverse problems in seismology 
D. Zhang, Peking University, Beijing, P.R. China 
Stochastic uncertainty modeling
Editorial Board:
V. Cvetkovic, Royal Institute of Technology, Stockholm, Sweden 
Stochastic approach to flow 
M. Delshad, University of Texas, TX, USA
Reservoir Engineering
L. Durlofsky, Stanford University, CA, USA
Reservoir simulation, upscaling, production optimization, reduced-order modeling, fractured-reservoir modeling
R. Helmig, University of Stuttgart, Germany 
Environmental fluid mechanics
J.D. Jansen, DelftUniversity of Technology, The Netherlands 
Reservoir simulation, optimization, data assimilation, smart fields 
T. Mannseth, NORCE Norwegian Research Centre, Bergen, Norway
Inverse problems, data assimilation
W.A. Mulder, Shell Global Solutions International, Rijswijk, The Netherlands