138 CS13 Abstracts IP1 the most striking examples of a successful integrative multi- Control and Optimization of Subsurface Flow scale modeling approach applied to a living system directly relevant to human disease. This presentation showcases Controlling the flow of fluids (e.g. water, oil, natural gas specific examples of the state-of-the-art in cardiac integra- or CO2) in subsurface porous media is a technical pro- tive modeling, including 1) improving ventricular ablation cess with many mathematical challenges. The underlying procedure by using MRI reconstructed heart geometry and physics can be described with coupled nearly-elliptic and structure to investigate the reentrant circuits formed in the nearly-hyperbolic nonlinear partial differential equations, presence of an infarct scar; 2) developing a new out-of- which require the aid of large-scale numerical simulation. the box high-frequency defibrillation methodology; 3) un- The strongly heterogeneous nature of subsurface rock leads derstanding the contributions of non-myocytes to cardiac to strong spatial variations in the coefficients. Moreover, function and dysfunction, and others. the limited accessibility of the underground leads to very large uncertainties in those coefficients and severely lim- Natalia A. Trayanova its the amount of control over the dynamic variables. In John Hopkins University this talk I will address related system-theoretical aspects, Institute for Computational Medicine reduced-order modeling techniques, and adjoint-based op- [email protected] timization methods. Jan Dirk Jansen IP5 TU Delft, The Netherlands Quantum Mechanics Without Wavefunctions ’[email protected]’ In principle, predictions of the electronic structure of atoms, molecules, and materials requires solving the many- IP2 body Schrdinger wave equation (SWE), whose eigenvalues Analyzing and Generating BIG Networks and eigenfunctions delineate the distribution of electrons in energy and space, respectively. In fact, the SWE cannot Graphs and networks are used to model interactions in a be solved exactly for more than one electron but excellent variety of contexts, and there is a growing need to accu- approximations are available. However, such methods scale rately measure and model large-scale networks. We con- extremely poorly with system size and generally are not ap- sider especially the role of triangles, useful for measuring plicable to large numbers of atoms or to condensed matter. social cohesion. This talk will focus on two topics: (1) Ac- Alternatively, one can solve directly for the electron den- curately estimating the number of triangles and clustering sity distribution rather than the many-body wavefunction, coefficients for BIG networks, and (2) Generating BIG ar- within orbital-free density functional theory (OFDFT). By tificial networks that capture the degree distribution and so doing, the problem greatly simplifies to 3 degrees of clustering coefficients of observed data. This is joint work freedom rather 3N (N being the number of electrons). The with Ali Pinar, Todd Plantenga, and C. Seshadhri from state of the art of OFDFT, in terms of algorithms, physical Sandia National Labs and Christine Task from Purdue Uni- models, and applications, will be discussed. versity. Emily A. Carter Tamara G. Kolda Princeton University Sandia National Laboratories [email protected] [email protected] IP6 IP3 Automated Astrophysics in the Big Data Era Certified Reduced Models and Their Applications Telescope projects are now routinely obtaining massive dig- Models of reduced computational complexity are used ex- ital movies of the dynamic nights sky. But given the grow- tensively throughout science and engineering to facilitate ing data volumes, coupled with the need to respond to tran- modeling of complex systems for control, design, multi- sient events quickly with appropriate followup resources, scale analysis, uncertainty quantification etc. We shall dis- it is no longer possible for people to be involved in the cuss ongoing efforts to develop reduced methods endowed real-time loop. I discuss the development of a robotic tele- with rigorous error estimators to certify models, hence scopes, autonomous follow-up networks, and a machine- endowing it with predictive value. We outline the basic learning framework that act as a scalable, deterministic ideas behind certified models and discuss computational human surrogate for discovery and classification in astro- efficiency and efficient model construction. The perfor- nomical imaging. mance of the certified reduced models will be illustrated through several examples and, time permitting, we con- Joshua S. Bloom clude by discussing some ideas aiming to enable the devel- University of California, Berkeley opment of certified reduced models for high-dimensional [email protected] parametrized problems. Jan S. Hesthaven IP7 Brown University PDE-Based Simulation Beyond Petascale Division of Applied Mathematic [email protected] We explore fundamental computational complexity con- siderations that will drive algorithmic design choices for PDE-based simulation codes scaling to petascale and be- IP4 yond. We argue that high-order methods using implicit Modeling Cardiac Function and Dysfunction or semi-implicit solvers are essential to efficient simulation of multiscale problems. These methods can be realized Simulating cardiac electrophysiological function is one of at per-point-costs equivalent low-order methods. We fur- CS13 Abstracts 139 ther show that multilevel solvers having bounded iteration presented in several in-vivo datasets. counts can scale to billion-way concurrency. We analyze the scalability of (low- or high-order) domain decomposi- David Fuentes, Samuel Fahrenholtz, John Hazle, Jason tion approaches to predict parallel performance on exascale Stafford architectures. These predictions shed light on what exas- MD Anderson Cancer Center cale CFD computation might enable and provide insight [email protected], [email protected], jha- to design requirements of exascale algorithms, codes, and [email protected], jstaff[email protected] architectures. Paul F. Fischer CP1 Argonne National Laboratory Renewables Integration in Power System Opera- fi[email protected] tions Modeling The requirements for a smart, green, efficient and reliable IP8 grid are reshaping the US power grid. The integration Challenges for Algorithms and Software at Ex- of renewable resources in the power system operations in- treme Scale troduces further complexity to utilities profit optimization problem. The Renewable Integration Model (RIM) a com- Extreme scale systems face many challenges. The end of puter based system - offers insight on power systems man- frequency scaling forces the use of extreme amounts of con- agement, and answers questions on the integration of re- currency. Power constraints are forcing a reconsideration newable energies. The RIM’s design, solution method, and of the processor architecture, eliminating features that pro- initial results are studied in the following. vide small performance benefit relative to the power con- sumed and making use of specialized processing elements Cristina Marinovici such as GPUs. Future systems will need to combine these Pacific Northwest National Laboratory and other approaches to approach Exascale performance. [email protected] This talk discusses how algorithms and software need to change to make effective use of extreme scale systems. Harold Kirkham Pacific Northwest National Laboratory William D. Gropp Richland WA University of Illinois at Urbana-Champaign [email protected] Dept of Computer Science [email protected] Leif Carlsen PNNL IP9 [email protected] Consistent Modelling of Interface Conditions for Multi-Physics Applications Kevin Glass Pacific Northwest National Laboratory In many multi-physics applications, information transport Richland WA plays an important role for the efficiency and stability of [email protected] the applied numerical algorithms. The global accuracy is quite often dominated by local effects at the interfaces, and local singularities can pollute the numerical solution. CP1 Here we discuss several issues such as hierarchical limiting Heel Effect Adaptive Gain Correction of Flat Panel techniques, local energy corrections and optimal estimates X-Ray Detectors for the flux variables. The coupling is controlled by pairs of balance equations providing a very flexible framework. Anode heel effect causes large-scale variations in the X-ray Different examples illustrate the abstract concepts, special beam intensity. Together with the localized gain variability focus is on surface based coupling techniques and highly of the detector, they contribute to the non-uniformities of non-linear dimension-reduced systems. digital radiographs. For mobile applications, conventional flat field gain correction techniques degrade substantially Barbara Wohlmuth without recalibration after each source-to-image distance M2, Centre for Mathematical Sciences, (SID) reset. We present a novel method to adaptively com- Technische Universit¨at M¨unchen, Germany pensate for the heel effect with flat field techniques and [email protected] minimize the effort for recalibration. The method is based on a mathematical-physical model to estimate the heel ef- fect ratio at two different SIDs. The gain factor calibrated CP1 at the standard SID is applied to any setting with heel ef- Planning of MR-Guided Laser Induced Thermal fect compensation. The model parameters are determined Therapy Using UQ Methods