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JOURNAL OF COMPUTATIONAL PHYSICS

AUTHOR INFORMATION PACK

TABLE OF CONTENTS

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Description Audience Impact Factor Abstracting and Indexing Editorial Board Guide for Authors p.1 p.2 p.2 p.2 p.2 p.6

ISSN: 0021-9991

DESCRIPTION

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Journal of Computational Physics has an open access mirror journal Journal of Computational Physics:

X which has the same aims and scope, editorial board and peer-review process. To submit to Journal

of Computational Physics: X visit https://www.editorialmanager.com/JCPX/default.aspx.

The Journal of Computational Physics focuses on the computational aspects of physical problems. JCP encourages original scientific contributions in advanced mathematical and

numerical modeling reflecting a combination of concepts, methods and principles which are often

interdisciplinary in nature and span several areas of physics, mechanics, applied mathematics, statistics, applied geometry, computer science, chemistry and other scientific disciplines as

well: the Journal's editors seek to emphasize methods that cross disciplinary boundaries.

The Journal of Computational Physics also publishes short notes of 4 pages or less (including

figures, tables, and references but excluding title pages). Letters to the Editor commenting on articles already published in this Journal will also be considered. Neither notes nor letters should have an abstract. Review articles providing a survey of particular fields are particularly encouraged. Full

text articles have a recommended length of 35 pages. In order to estimate the page limit, please

use our template. Published conference papers are welcome provided the submitted manuscript is a significant enhancement of the conference paper with substantial additions.

Reproducibility, that is the ability to reproduce results obtained by others, is a core principle of the scientific method. As the impact of and knowledge discovery enabled by computational science and engineering continues to increase, it is imperative that reproducibility becomes a natural part of these activities. The journal strongly encourages authors to make available all software or data that would allow published results to be reproduced and that every effort is made to include sufficient information in manuscripts to enable this. This should not only include information used for setup but also details on post-processing to recover published results.

You can link to data posted in a repository or upload them to Mendeley Data. We also encourage authors to submit research elements describing their data to Data in Brief and software to Software X.

Benefits to authors

We also provide many author benefits, such as free PDFs, a liberal copyright policy, special discounts on Elsevier publications and much more. Please click here for more information on our author services.

Please see our Guide for Authors for information on article submission. If you require any further information or help, please visit our Support Center

AUDIENCE

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Experimental and theoretical physicists and chemists working with computational techniques.

IMPACT FACTOR

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2020: 3.553 © Clarivate Analytics Journal Citation Reports 2021

ABSTRACTING AND INDEXING

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Scopus ACM Guide to Computing Literature CompuMath Citation Index Chemical Abstracts Current Contents - Physical, Chemical & Earth Sciences Embase Mathematical Reviews Research Alert Science Abstracts Science Citation Index Zentralblatt MATH INSPEC

EDITORIAL BOARD

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Editor in Chief

Rémi Abgrall, University of Zurich, Zurich Switzerland

Numerical schemes for hyperbolic problems; Multiphase flows; Interface problems; Hamilton Jacobi; Unstructured meshes; High order schemes

Executive Editors

Nikolaus Adams, Technical University of Munich Chair of Aerodynamics and Fluid mechanics, Garching, Germany

Flow physics; Modelling and simulation of multi-scale flows and complex fluids; Dissemination of fundamental research into applications.

Luis Chacon, Los Alamos National Laboratory, Los Alamos, New Mexico, United States of America

Computational physics algorithm development and implementation; Computational co-design; Computational and theoretical plasma physics at the macroscopic scale, the kinetic scale, and across these scales (multi-scale); Magnetic reconnection; Magnetic confinement; Inertial confinement.

Feng Xiao, Tokyo Institute of Technology - Suzukakedai Campus, Yokohama, Japan

Computational fluid dynamics, Interfacial multiphase flows, Compressible and incompressible flows, Unified approach, Finite volume method, Unstructured grids, Numerical modeling for geophysical fluid dynamics

Associate Editors

Habib Ammari, ETH Zurich, Zurich, Switzerland

Mathematical fundamentals for metamaterials and topological properties at subwavelength scales, Super-resolution imaging, Hybrid medical imaging, Mathematics for bio-inspired imaging

Dinshaw Balsara, University of Notre Dame, Notre Dame, Indiana, United States of America

Computational physics; Computational astrophysics; High performance computation

Alvin Bayliss, Northwestern University, Evanston, Illinois, United States of America

Numerical methods for partial differential equations; Spectral and finite difference methods; Computational wave propagation

Liliana Borcea, University of Michigan Department of Mathematics, Ann Arbor, Michigan, United States of America

Inverse problems, waves in random media, reduced order models

Tan Bui-Thanh, The University of Texas at Austin, The Oden Institute for Computational Engineering and Sciences Department of Aerospace Engineering and Engineering Mechanics, Austin, Texas, United States of America

Inverse problems, Uncertainty quantification. High-order (DG/HDG) FEM methods, Reduced-order modeling (model order reduction), Model-aware Machine Learning

Juan Cheng, Institute of Applied Physics and Computational Mathematics, Beijing, China

Lagrangian and Arbitrary-Lagrangian-Eulerian (ALE) method for compressible hydrodynamics equations; Numerical method for radiation transfer equations; High order numerical method for hyperbolic conservation laws

Eric Darve, Stanford University, Stanford, California, United States of America

Numerical linear algebra, machine learning for computational engineering, parallel computing, hierarchical solvers (LU, QR factorizations) for large sparse matrices, task-based parallel programming systems

Giacomo Dimarco, University of Ferrara Department of Mathematics and Computer Science, Ferrara, Italy

Kinetic equations; Monte Carlo methods; Multiscale numerical methods; Collective dynamics; SelfOrganization; Uncertainty quantification; Computational plasma Physics

Yalchin Efendiev, Texas A&M University Department of Mathematics, College Station, Texas, United States of America

Numerical analysis, Scientific Computing, Multiscale Simulation, Uncertainty Quantification

Ron Fedkiw, Stanford University, Stanford, California, United States of America

Solid-fluid coupling; Interfaces; Compressible flow; Incompressible flow

Frederic G. Gibou, University of California Santa Barbara, Santa Barbara, California, United States of America

Level set methods; Finite difference/volume approximations of PDEs; Parallel computing

Jan Hesthaven, Federal Polytechnic School of Lausanne, Lausanne, Switzerland

Numerical methods for PDE’s; High-order methods; Absorbing boundary conditions; Reduced order modeling; Wave-problems; Conservation laws; Computational electromagnetics

Gianluca Iaccarino, Stanford University, Stanford, California, United States of America

Computational Fluid Dynamics; Immersed Boundary Methods; Uncertainty Quantification; Turbulence Modeling

Shi Jin, Shanghai Jiao Tong University - Fahua Campus, Shanghai, China

Kinetic equations, hyperbolic conservation laws, quantum dynamics, high frequency waves, uncertainty quantification

George E. Karniadakis, Brown University, Providence, Rhode Island, United States of America

Stochastic multiscale methods; Uncertainty quantification; Fractional PDEs; Atomistic methods; Spectral and spectral element methods; Machine learning

Barry Koren, Eindhoven University of Technology, Eindhoven, Netherlands

Scientific Computing; Computational Fluid Dynamics

Tony Lelievre, National College of Civil Engineering, Marne La Vallee, France

Computational statistical physics, Rare event sampling, Free energy calculation, Metastability, Model Order Reduction, quantum Monte carlo methods, Free surface flow, Multiscale models of complex fluids

Lin Lin, University of California Berkeley Department of Mathematics, Berkeley, California, United States of America

Electronic structure theory; Quantum many-body physics; Quantum computation

Li-Shi Luo, Old Dominion University, Norfolk, Virginia, United States of America

Kinetic methods for CFD (lattice Boltzmann equation, lattice gas automata, and gas-kinetic scheme); Kinetic theory and non-equilibrium statistical mechanics; non-equilibrium and complex fluids; DNS and LES of turbulence

Karel Matouš, University of Notre Dame, Notre Dame, Indiana, United States of America

Predictive computational science and engineering at multiple spatial and temporal scales including multi-physics interactions; Development of advanced numerical methods; High performance parallel computing.

Rajat Mittal, Johns Hopkins University, Baltimore, Maryland, United States of America

Computational fluid dynamics; Biofluid dynamics; Fluid-structure interaction; Flow control; Biomimetics and immersed boundary methods

Parviz Moin, Stanford University, Stanford, California, United States of America

Computational fluid dynamics; High fidelity numerical simulation of multi-physics turbulent flows

Jim Morel, Texas A&M University, College Station, Texas, United States of America

Deterministic, Monte Carlo, and hybrid deterministic/Monte Carlo methods for neutral and chargedparticle transport; Radiation-hydrodynamics methods

Jan Nordström, Linköping University, Linköping, Sweden

Initial boundary value problems; Boundary and interface conditions; High order methods; Wellposedness and stability; Wave and uncertainty propagation

Stanley Osher, University of California Los Angeles, Los Angeles, California, United States of America

Nonlinear hyperbolic equations; Level set methods; Image and information processing; Optimization

Jianxian Qiu, Xiamen University, Xiamen, China

Numerical solutions of conservation laws and in general convection dominated problems using: Finite difference essentially non-oscillatory (ENO) methods and weighted ENO (WENO) methods, Finite element discontinuous Galerkin methods (DG), Numerical solution of Hamilton-Jacobi type equations, Computational fluid dynamics, Simulations of multi-phase flow using DG and WENO method

Mario Ricchiuto, French Institute for Research in Computer Science and Automation, Rocquencourt, France

Numerical methods for partial differential equations, Hyperbolic balance laws, Unstructured grids, Residual distribution and finite element methods, Immersed and embedded methods, Free surface flows, Geophysical flows, Compressible flows

Pierre Sagaut, Laboratory of Mechanics Modelling and Clean Processes, Marseille, France Guglielmo Scovazzi, Duke University Department of Civil and Environmental Engineering, Durham, North Carolina, United States of America

Finite element methods, Computational fluid and solid mechanics, Multiphase porous media flows, Computational methods for fluid and solid materials under extreme load conditions, Turbulent flow computations, Instability phenomena

Mikhail Shashkov, Los Alamos National Laboratory, Los Alamos, New Mexico, United States of America

Modeling high-speed multi-material compressible flows; Meshing; PDE discretization methods; Interface Reconstruction

Chi-Wang Shu, Brown University, Providence, Rhode Island, United States of America

Finite difference; Finite volume and finite element methods; Computational fluid dynamics

Piotr Smolarkiewicz, National Center for Atmospheric Research, Boulder, Colorado, United States of America

Scientific computing; Geophysical flows of all scales; Solar dynamo; Non-Newtonian fluids; Dynamics of continuous media

Eric Sonnendrücker, Max-Planck-Institute of Plasma Physics, Garching, Germany

Computational plasma physics; modelling and simulation of magnetic confinement fusion; numerical methods for kinetic, gyrokinetic and fluid plasma models; geometric and structure preserving numerical methods

Mark Sussman, Florida State University, Tallahassee, Florida, United States of America

Multiphase Flows, Deforming boundary problems, Adaptive Mesh Refinement, Transport processes on Fluidic Interfaces, High performance Computing

Huazhong Tang, Peking University, Beijing, China Tao Tang, Southern University of Science and Technology, Shenzhen, China

Spectral and high order methods; Adaptive methods; Computational fluid dynamics.

Chrysoula Tsogka, University of California Merced, Merced, California, United States of America

wave propagation, random media, coherent interferometry, imaging, time reversal

Eli Turkel, Tel Aviv University, Tel Aviv, Israel

Fast acceleration algorithms for Navier Stokes; High order compact methods for wave equation in general shaped domains; Time Reversal for source and obstacle location; Reading ostraca from first Temple era

Karen Veroy-Grepl, RWTH Aachen University, Aachen, Germany

Numerical methods for partial differential equations, Model order reduction and its use in optimization, Uncertainty quantification and data assimilation, as well as development and application of these methods for problems in medicine, Heat transfer, Solid and fluid mechanics and Multi-scale materials engineering.

Jens Honore Walther, Technical University of Denmark, Kgs Lyngby, Denmark Dongbin Xiu, The Ohio State University, Columbus, Ohio, United States of America

Uncertainty quantification; Approximation theory; Data assimilation

Nicholas Zabaras, University of Notre Dame, Notre Dame, Indiana, United States of America

Artificial Intelligence, Scientific Computing, Machine Learning, Deep Learning, Inverse Problems, Data Science, Computational Mathematics, Computational Statistics

Stephane Zaleski, Sorbonne University, Paris, France

Volume-of-Fluid Method; Multiphase Flow; Surface Tension; Interface Tracking; Free Surface

Editors Emeriti

Jeremiah U. Brackbill John Kileen

Phil L. Roe Gretar Tryggvason

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  • Computational Science, Ph.D. 1

    Computational Science, Ph.D. 1

    Computational Science, Ph.D. 1 Computational Science, Ph.D. Lee Swindlehurst, UCI Director 949-824-2818 computationalscience.uci.edu (https://computationalscience.uci.edu/) Joint Doctoral Program with UC Irvine and San Diego State University A joint offering with San Diego State University (SDSU), the Ph.D. program in Computational Science trains professionals capable of developing novel computational approaches to solve complex problems in both fundamental sciences and applied sciences and engineering. A program of study combining applied mathematics, computing, and a solid training in basic science culminates in doctoral research focused on an unsolved scientific problem. The Ph.D. in Computational Science produces broadly educated, research-capable scientists that are well prepared for diverse careers in academia, industry, business, and government research laboratories. Students are admitted into the joint program via a Joint Admissions Committee. Applicants apply to UCI directly using the UCI graduate application. Applicants are expected to hold a Bachelor’s degree in one of the science, technology, engineering, and mathematics (STEM) fields. Applicants are evaluated on the basis of their prior academic record and their potential for creative research and teaching, as demonstrated in submitted materials. These materials include official university transcripts, three letters of recommendation, GRE scores, a Statement of Purpose, and a Personal History statement. Program Requirements The normative time to completion is five years. The maximum time to completion is seven years. A total minimum of 66 units of course work, independent study, and research must be completed. These units must be distributed as follows: • Minimum of 18 units of graduate-level coursework as SDSU.