Cyber-enabled Discovery and Innovation (CDI)

1. Cyber-enabledDiscovery and Innovation (CDI) Objective: Enhance American competitiveness by enabling innovation through the use of computational thinking (concepts, methods, models, algorithms, tools)

2. CDI is Unique within NSF • five-year initiative • all directorates, programmatic offices involved • to create revolutionary science and engineering research outcomes • made possible by innovations and advances in computational thinking • emphasis on bold, multidisciplinary activities • radical, paradigm-changing science and engineering outcomes through computational thinking

3. CDI Philosophy • “Business as usual” need not apply • “Projects that make straightforward use of existing computational concepts, methods, models, algorithms and tools to significantly advance only one discipline should be submitted to an appropriate program in that field instead of to CDI.” • No place for incremental research • Untraditional approaches and collaborations welcome

4. NSF Review Criteria • Intellectual Merit • Broader Impacts • New on Transformative Research: to what extent does the proposed activity suggest and explore creative, original, or potentially transformative concepts?

5. Additional CDI Review Criteria • The proposal should define a bold multidisciplinary research agenda that, through computational thinking, promises paradigm-shifting outcomes in more than one field of science and engineering. • The proposal should provide a clear and compelling rationale that describes how innovations in, and/or innovative use of, computational thinking will lead to the desired project outcomes.  • The proposal should draw on productive intellectual partnerships that capitalize upon knowledge and expertise synergies in multiple fields or sub-fields in science or engineering and/or in multiple types of organizations. • Potential for extraordinary outcomes, such as • revolutionizing entire disciplines, • creating entirely new fields, or • disrupting accepted theories and perspectives … as a result of taking a fresh, multi-disciplinary approach.  Special emphasis will be placed on proposals that promise to enhance competitiveness, innovation, or safety and security in the United States.

6. Three CDI Themes CDI seeks transformative research in the following general themes, via innovations in, and/or innovative use of, computational thinking: • From Data to Knowledge:enhancing human cognition and generating new knowledge from a wealth of heterogeneous digital data; • Understanding Complexity in Natural, Built, and Social Systems:deriving fundamentalinsights onsystems comprising multiple interacting elements;  and • Building Virtual Organizations:enhancing discovery and innovation bybringing people and resources together across institutional, geographical and cultural boundaries. 

7. From Data to Knowledge • Knowledge extraction, noise, statistics • Modeling, data assimilation, inverse problems • Validation; model/cyber/domain feedbacks • Algorithms for analysis of large data sets, dimension reduction • Visualization, pattern recognition

8. Understanding Complexity in Natural, Built, and Social Systems Identifying general principles and laws that characterize complexity and capture the essence of complex systems Attaining the breakthroughs, to overcome these challenges, requires transformative ideas in the following areas: • Simulation and Computational Experiments • Methods, Algorithms, and Tools • Nonlinear couplings across multiple scales

9. Virtual Organizations (VOs) Design, development, and assessment of VOs Bringing domain needs together with algorithm development, systems operations, organizational studies, social computing, and interactive design Flexible boundaries, memberships, and lifecycles, tailored to particular research problems, users and learner needs or tasks of any community, providing opportunities for: • Remote access • Collaboration • Education and training

10. Types of Projects • CDI defines research modalities • Project size not measured by $$ • Projects classified by magnitude of effort • Three types are defined: Types I (~2 PI, 2 GRA), II (~3 PI, 3 GRA, 1 post-doc), and III (center scale) • Type III, center-scale efforts, will not be supported in the first year of CDI

11. Funding Projections and Profile of NSF-Wide FY2008 Competition All NSF directorates and programmatic offices are participating in CDI FY2008 FY2009 FY2010 FY2011 FY2012 (budgeted) (requested) ------------------ (projected) ----------------- NSF $47.9 M* $100 M $150 M $200 M $250 M MPS $10.4 M $19.05 M ? ? ? *At least$26 M ($5.2 M MPS)of the$47.9 M ($10.4 M MPS)to be invested in the NSF-wide pooled solicitation for FY08 ----------------------------------------------------------------------------------------- Type I (mainly small collaborations): 494 reviewed, 82 invited (16%) Type II (comparable to focused research groups): 743 reviewed, 122 invited (16%) Full proposals due April 29, 2008; to be reviewed early June Expect ≈ 30 awards (Type I + Type II) made in July 2008

12. Stochastic Modeling of Multiphase Transport in Subsurface Porous Media: Motivation and Some Formulations Thomas F. Russell National Science Foundation, Division of Mathematical Sciences David Dean, Tissa Illangasekare, Kevin Barnhart Multiscale Workshop, Oregon State University, June 25-29, 2007

13. Philosophy 1 • Goal: (motivated, e.g., by DNAPL) Macro-model of complex multiphase transport – pooling, fingering, etc. – amenable to efficient computation • Pore-scale physics very important, but won’t be seen in that form at macro-scale • Homogenization can yield important insights, but is too restricted

14. Philosophy 2 • For practical purposes, heterogeneous multiphase effects can’t be characterized deterministically • Micro-scale phenomena show traits of randomness when viewed through a coarser lens • Thus: Consider modeling with stochastic processes • Seek: Stochastic micro-model that yields macro capillary behavior (end effect, etc.), analogous to Einstein-Fokker-Planck

15. Outline • Background on capillary end effect • 2-phase stochastic transport equation for position of nonwetting fluid particle, derived from Itô calculus • Numerical SPDE • Capillary barrier effect • Channeling / fingering • Qualitatively capture experimental behavior

16. Summary • Propose The Use Of The Ito Calculus To Develop Stochastic • Differential Equation (SDE) Descriptions Of Saturation Phases • Test The Ability Of The SDE Model To Capture The Interface Effects • Of Plume Development, Such As Pooling, Channeling And Fingering • Extend This Work To A Nonlinear Up-scaling Methodology • Develop A Macro-scale Stochastic Theory Of Multiphase Flow And • Transport Accounting For Micro-scale Heterogeneities And Interfaces