CONTENTS -- FOREWORD -- PREFACE -- ABSTRACT -- ADDENDUM -- EXECUTIVE SUMMARY -- 1 Background -- 2 Major Trends in SBE&S Research and Development -- 3 Threats to U.S. Leadership in SBE&S -- 4 Opportunities for the United States to Gain or Reinforce Lead in SBE&S through Strategic Research and Investments -- 5 Key Study Findings -- 5.1 Thematic Area: Life Sciences and Medicine -- 5.2 Thematic Area: Materials -- 5.3 Thematic Area: Energy and Sustainability -- 5.4 Crosscutting Issues: Next Generation Architectures and Algorithms -- 5.5 Crosscutting Issues: Scientific and Engineering Simulation Software Development -- 5.6 Crosscutting Issues: Engineering Simulation -- 5.7 Crosscutting Issues: Validation, Verification, and Uncertainty Quantification -- 5.8 Crosscutting Issues: Multiscale Modeling and Simulation -- 5.9 Crosscutting Issues: Big Data, Visualization, and Data-driven Simulation -- 5.10 Crosscutting Issues: Education and Training -- References -- Further Reading -- Chapter 1 INTRODUCTION -- 1.1 Background and Scope -- 1.2 Methodology -- 1.3 Overview of the Report -- References -- Chapter 2 LIFE SCIENCES AND MEDICINE -- 2.1 Introduction -- 2.2 Molecular Dynamics -- 2.3 Systems Biology -- 2.3.1 Systems Biology Institute, Japan -- 2.3.2 Vrije University, The Netherlands -- 2.3.3 Technical University of Denmark -- 2.3.4 U.S. Systems Biology Efforts -- 2.4 Biophysical Modeling -- 2.4.1 International Physiome Project -- 2.4.2 EPFL Arterial Map -- 2.4.3 EPFL Blue Brain Project -- 2.4.4 U.S. Biophysical Modeling Efforts -- 2.5 Summary of Key Findings -- References -- Chapter 3 MATERIALS SIMULATION -- 3.1 Introduction -- 3.2 Current State of the Art in Materials Simulation -- 3.3 Materials Simulation Code Development -- 3.4 Materials Simulation Highlights -- 3.4.1 Mitsubishi Chemical -- 3.4.2 Toyota Central R&D Labs, Inc.

3.4.3 Joint Laboratory of Polymer Science and Materials, Institute of Chemistry, Chinese Academy of Sciences -- 3.4.4 Materials Simulation Code Development in the UK -- 3.4.5 Fraunhofer Institute for the Mechanics of Materials -- 3.4.6 Energy Applications of Materials at Daresbury Laboratory -- 3.5 Summary of Key Findings -- References -- Chapter 4 ENERGY AND SUSTAINABILITY -- 4.1 Introduction -- 4.2 SBE&S Research Activities in North America -- 4.2.1 Analysis of Energy and CO2 Emission -- 4.2.2 Modeling System Reliability of Electric Power Networks -- 4.2.3 Modeling Civil Infrastructure Systems Resilience and Sustainability -- 4.3 SBE&S Research Activities in Asia -- 4.3.1 Modeling System Reliability of the Electric Power Network -- 4.3.1.1 National Center for Research on Earthquake Engineering (NCREE), Taipei Taiwan -- 4.3.1.2 Central Research Institute for Electric Power Industry (CRIEPI), Tokyo, Japan, and Chugoku Electric Power, Hiroshima, Japan -- 4.3.2 Modeling Civil Infrastructure Systems Resilience and Sustainability -- 4.3.2.1 Pusan Port Authority, Pusan, Korea -- 4.3.2.2 Disaster Control Research Center, School of Engineering, Tohoku University, Japan -- 4.3.2.3 Osaka University Department of Management of Industry and Technology -- 4.3.3 Energy Related Modeling -- 4.3.3.1 Toyota Central R&D Labs Inc. (TCRDL), Japan -- 4.3.3.2 Fuel Cell Laboratory, Nissan Research Center 1, Japan -- 4.3.3.3 Central Research Institute of Electric Power Industry (CRIEPI), Japan -- 4.4 Research Activities in Europe -- 4.4.1 System Reliability of Electric Power Network -- 4.4.1.1 Union for the Coordination of Transmission of Electricity, Brussels, Belgium -- 4.4.2 Energy-Related Simulation Research -- 4.4.2.1 Technical University of Denmark (DTU), Wind Engineering, Department of Mechanical Engineering (MEK), Denmark.

4.4.2.2 Institut Français du Pétrole (IFP), France -- 4.4.2.3 Science and Technology Facilities Council (STFC) Daresbury Laboratory (DL), Warrington, United Kingdom -- 4.4.3 Modeling Civil Infrastructure Systems Resilience and Sustainability -- 4.4.3.1 University of Oxford, Oxford, United Kingdom -- 4.5 Conclusions -- References -- Chapter 5 NEXT-GENERATION ARCHITECTURES AND ALGORITHMS -- 5.1 Introduction -- 5.2 High-Performance Computing Around the World -- 5.2.1 High-Performance Computing in the United States -- 5.2.2 High-Performance Computing in Japan -- 5.2.3 High-Performance Computing in Europe -- 5.2.4 High-Performance Computing in China -- 5.2.5 High-Performance Computing in India -- 5.2.6 Future Directions for High-Performance Computing -- 5.2.6.1 Looking to Exascale -- 5.2.6.2 Special-Purpose Processors -- 5.3 New Programming Languages -- 5.4 The Scalability Bottleneck -- 5.5 Summary of Findings -- Acknowledgements -- References -- Chapter 6 SOFTWARE DEVELOPMENT -- 6.1 Introduction -- 6.2 Role of Universities, National Laboratories, and Government -- 6.3 Software Life Cycle - Managing Complexity -- 6.4 Supercomputing Software Versus Software for Midrange Computing -- 6.5 World Trends in Simulation Software Development -- 6.6 Comparative Aspects of Funding for Applications Software Development -- 6.7 Emerging Opportunities in Software Development -- 6.8 Summary of Findings -- References -- Chapter 7 ENGINEERING SIMULATIONS -- 7.1 Introduction -- 7.2 Engineering Simulation Highlights -- 7.2.1 Energy Systems -- 7.2.2 Disaster Planning -- 7.2.3 Product and Process Modeling -- 7.2.4 Computational Fluid Dynamics (CFD) -- 7.3 Summary of Key Findings -- 7.4 Comparison of U.S. and Worldwide Engineering Simulation Activities -- References -- Chapter 8 VERIFICATION, VALIDATION, AND UNCERTAINTY QUANTIFICATION -- 8.1 Introduction.

8.2 Effects of Uncertainty Propagation -- 8.2.1 Fluid Mechanics -- 8.2.2 Plasma Dynamics -- 8.2.3 Biomedical Applications -- 8.3 Methods -- 8.3.1 Engineering Systems -- 8.3.1.1 Materials -- 8.3.1.2 Uncertainty-Based Design -- 8.3.1.3 Certification/Accreditation -- 8.3.2 Molecular Systems -- 8.4 Industrial View -- 8.5 Summary of Findings -- References -- Chapter 9 MULTISCALE SIMULATION -- 9.1 Introduction -- 9.2 Current State of the Art in Multiscale Simulation -- 9.3 Multiscale Simulation Highlights -- 9.3.1 Mitsubishi Chemical -- 9.3.2 Theory of Condensed Matter Group, Cavendish Laboratory, Cambridge University -- 9.3.3 Blue Brain Project, École Polytechnique Fédérale de Lausanne -- 9.3.4 Yoshimura Group, Department of Systems Innovation, School of Engineering, University of Tokyo -- 9.4 Summary of Key Findings -- References -- Chapter 10 BIG DATA, VISUALIZATION, AND DATA-DRIVEN SIMULATIONS -- 10.1 Introduction -- 10.2 Particle Physics Research: Petabytes Per Second -- 10.3 Big Data in Life Sciences Research -- 10.3.1 Ecole Polytechnique Federale de Lausanne, Blue Brain Project - Switzerland -- 10.3.2 Daresbury Laboratory/Science and Technology Facilities Council, e-HPTX - United Kingdom -- 10.3.3 Systems Biology Institute - Tokyo, Japan -- 10.3.4 Earth Simulator Center and Plans for the Life Simulator - Japan -- 10.4 Big Data in Industry: Enterprise-Scale Knowledge Integration -- 10.5 Big Data - The Road Ahead for Training and Education -- 10.6 Summary of Key Findings -- References -- Chapter 11 EDUCATION AND TRAINING -- 11.1 Introduction -- 11.2 Where the United States Stands -- 11.3 How Other Countries Compare -- 11.3.1 Finding 1: There is Increasing Asian and European Leadership in SBE&S Education due to Dedicated Funding Allocation and Industrial Participation.

11.3.2 Finding 2: There are a Number of New EU Centers and Programs for Education and Training in SBE&S - All of them of an Interdisciplinary Nature -- 11.3.3 Finding 3: EU and Asian Education/Research Centers are Attracting an Increasing Number of International Students from All Over the World, Including the United States -- 11.3.4 Finding 4: There are Pitfalls Associated with Interdisciplinary Education: Breadth Versus Depth -- 11.3.5 Finding 5: Demand Exceeds Supply: Academia Versus Industry -- 11.3.6 Finding 6: There is Widespread Difficulty Finding Students and Postdocs Qualified in Algorithmic and Software Development -- 11.3.7 Finding 7: Population Matters -- 11.4 Case Study: The University of Stuttgart - A Success Story -- 11.4.1 The SimTech Excellence Cluster -- 11.5 Conclusions -- References -- Appendix A BIOGRAPHIES OF PANELISTS AND ADVISORS -- A.1 Panelists -- A.1.1 Sharon C. Glotzer (Chair) -- A.1.2 Sangtae Kim (Vice Chair) -- A.1.3 Peter T. Cummings -- A.1.4 Abhijit Deshmukh -- A.1.5 Martin Head-Gordon -- A.1.6 George Em Karniadakis -- A.1.7 Linda Petzold -- A.1.8 Celeste Sagui -- A.1.9 Masanobu Shinozuka -- A.2 Advisors -- A.2.1 Tomás Díaz de la Rubia -- A.2.2 Jack Dongarra -- A.2.3 James Johnson Duderstadt -- A.2.4 J. Tinsley Oden -- A.2.5 Gilbert S. Omenn -- A.2.6 David E. Shaw -- A.2.7 Martin Wortman -- Appendix B SURVEY QUESTIONNAIRE -- B.1 General -- B.2 Materials/Energy and Sustainability/Life Sciences and Medicine -- B.3 Multiscale Simulation -- B.4 Validation, Verification, and Quantifying Uncertainty -- B.5 Simulation Software -- B.6 Big Data and Visualization -- B.7 Engineering Design -- B.8 Next-Generation Algorithms and High Performance Computing -- B.9 Education and Training -- B.10 Funding, Organization, and Collaboration -- Appendix C BIBLIOMETRIC ANALYSIS OF SIMULATION RESEARCH -- C.1 Introduction -- C.2 Methodology.

C.3 Results: National Comparisons.