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System Level Science and System Level Models

System Level Science and System Level Models. Ian Foster Argonne National Laboratory University of Chicago. Improving IAM Representations of a Science-Driven Energy Future , Snowmass, August 1-2, 2007

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System Level Science and System Level Models

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  1. System Level Scienceand System Level Models Ian Foster Argonne National LaboratoryUniversity of Chicago Improving IAM Representations of a Science-Driven Energy Future, Snowmass, August 1-2, 2007 Panel on “Alternative Modeling Perspectives: Challenges and Opportunities in Modeling Innovation, From Macro to Micro”

  2. System Level Science • Understanding in context • Move focus beyond individual phenomena • Understand how components interact and interrelate • Characteristics • End-to-end • Multi-disciplinary, multi-phenomena • Alternative approaches for each component • Often need to integrate rich data sources • Seek to answer many different types of questions

  3. Seismic Hazard Analysis (T. Jordan et al., SCEC) Seismicity Paleoseismology Geologic structure Local site effects Faults Seismic Hazard Model Stress transfer Rupture dynamics Crustal motion Crustal deformation Seismic velocity structure

  4. Astrophysics: The FLASH Code (U.Chicago)

  5. Systems Biology (storage) DNA Transcription Gene Expression Translation Proteins Proteomics Biochemical Circuitry Environment Metabolomics Phenotypes (Traits) Adapted from Bruno Sobral VBI

  6. Common Characteristics • Long-term project to tackle a complex problem • Construction of sophisticated modeling systems • Component-based to facilitate experimentation • Work performed by a multidisciplinary team • An inordinate focus on validation • Designed to use high-performance computing • Provided to the community as a resource • Used for many purposes • Advances the field substantially

  7. Future Directions • Sensitivity analysis • E.g., automated development of adjoint models • Data-intensive science—driven by “data big bang” • Peer-to-peer analysis and data product publishing • Distributed systems for automated analysis, discovery, and annotation • Automated hypothesis creation tools for pattern detection—capable of suggesting relationships • Predictive modeling

  8. Beyond Models: An Integrated View of Simulation, Experiment, & (Bio)informatics Problem Specification Simulation Browsing & Visualization SIMS* Database Analysis Tools LIMS+ Experiment Experimental Design Browsing & Visualization *Simulation Information Management System +Laboratory Information Management System

  9. Simulation and Modeling at the Exascalefor Energy, Ecological Sustainability and Global Security IBM

  10. Socio-Economic Modeling Terascale (i.e., today, almost) • Economic models with ~10 countries & ~10 sectors • Limited coupling with climate models • No treatment of uncertainty and business cycle risk • Simple impact analysis for a limited set of scenarios • Limited ability to provide quantitative policy advice

  11. Socio-Economic Modeling Petascale • Economic models with more countries, sectors, income groups • Limited treatment of uncertainty, business cycle risk • Stronger coupling with climate models

  12. Socio-Economic Modeling Exascale • Economic models with all countries, many sectors, many income groups • Many policy instruments (taxes, tariffs, quotas, CAFE, CO2 taxes), nonlinear policies, etc. • High spatial resolution in land use, etc. • Detailed coupling & feedbacks with climate models • Optimization of policy instruments & technology choices over time and with respect to uncertainty • Detailed model validation & careful data analysis • Treatment of technological innovation, industrial competition, population changes, migration, etc. Peta Tera

  13. Meta-Innovation: How Can We Accelerate Innovation? • We have discussed the usual ideas • Policies to encourage private R&D investment • Government investment in R&D • Education • Can we use technology to accelerate innovation? • Lack of innovators: engage the world (Wikipedia) • Access to information: a “US Knowledge Exchange” • Access to modeling: models, tools, supercomputers

  14. Earth System Grid

  15. Meta-Innovation: How Can We Accelerate Innovation? • We have discussed the usual ideas • Policies to encourage private R&D investment • Government investment in R&D • Education • Can we use technology to accelerate innovation? • Lack of innovators: engage the world (Wikipedia) • Access to information: a “US Knowledge Exchange” • Access to modeling: models, tools, computers

  16. Meta-Innovation: How Can We Accelerate Innovation? • We have discussed the usual ideas • Policies to encourage private R&D investment • Government investment in R&D • Education • Can we use technology to accelerate innovation? • Lack of innovators: engage the world (Wikipedia) • Access to information: a “US Knowledge Exchange” • Access to modeling: models, tools, supercomputers • How to represent technology-accelerated innovation?

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