Working Groups
This is a list of SAMSI working groups that you may request access to join. After you request to join a group, you must wait until your application has been approved by the working group administrator.
2011
| Working Group | Description | Join |
|---|---|---|
| Statistics of Extremes - Climate and Methodology UQ | This group will examine the characterization of extreme events from a statistical point-of-view. The overall group will be composed of several project groups, each with a specific focus. The project groups may be application-driven, may work develop new statistical methodologies, or may work to further the theory on which extreme value analyses rely. The overall group will provide a structure for the project groups as well as provide an environment for investigating aspects which have general interest. | View group |
| Parallel Computing Issues - Climate UQ | - adaptive design of experiments, and resolution issues - embedding of emulated sub-models to resolve sub-processes that are now computationally prohibitive - python open source software open platform | View group |
| Simulation of Rare Events - Methodology UQ | This group will focus on methods for simulating rare events in high-dimensional physical systems, especially PDE models. We will explore the use of importance sampling and large deviation theory in order to identify important mechanisms or configurations of the parameters that lead to rare events. We also will consider the role of asymptotic analysis in constructing effective sampling weights for such computations. | View group |
| Data Assimilation - Methodology UQ | Data assimilation is the process of fusing information from imperfect models, noisy measurements, and priors, to produce an optimal representation of the state of a physical system. Data assimilation can be interpreted and carried out in a Bayesian framework. Practical methods for large-scale systems include suboptimal and the ensemble Kalman filter approaches, optimal interpolation, and three and four dimensional variational methods. This working group will focus on emerging problems that include, but are not limited to: new computational algorithms, modeling of model errors, impact of observations, and quantification of posterior uncertainties. There will be strong ties between theory and applications investigated throughout the program. | View group |
| Stochastic to Deterministic Models and Back Again - Methodology UQ | Models of complex multiscale and/or multiphysics phenomena often require combining stochastic and deterministic models. Direct coupling of stochastic and deterministic models, e.g. molecular dynamics with a continuum model Stochastic parameterization, with parameters determined by a stochastic model simulation or other statistical models Such models are often used to predict “engineering scale” questions from limited microscale information. Of course, this is a classic analysis/modeling problem. The working group will focus on computational issues, including: Rigorous formulation and analysis of coupling mechanisms and their discretizations Numerical treatment of averaging and computed expectations and the effect of approximations A posteriori error analysis, resolutions required in different components, adaptive computation Rigorous treatment of feedback between stochastic and deterministic models, e.g. nonlinear iterative methods, convergence | View group |
| Model Validation - Methodology UQ | Model validation refers to the process of assessing the accuracy with which mathematical models can predict physical events, or, more specifically, quantities of interest observed in physical phenomena. Validation should be a prerequisite for predictive modeling, which often forms the basis for decision-making. This working group will study the principles, merits, and limitations of various probabilistic approaches to model validation. Special emphasis will be laid on methods for splitting datasets for calibration and validation purposes, on the analysis of model discrepancies, on the development of rejection metrics, and on any other issues of interest raised during the working group meetings. The working group is organized and managed by Serge Prudhomme (ICES, UT Austin), Sujit Ghosh (Statistics, NCSU), and Jan Hannig (STOR, UNC). | View group |
| Multiphysics - Methodology UQ | Multiphysics models comprising compositions of models of several physical processes, often at different scales, dominate many areas of science and engineering. The working group will study UQ topics for MP models (including both forward and inverse topics) Research issues: Complex feedback between physical processes, highly nonlinear responses Complex and unresolved coupling mechanisms Different kinds and representations of uncertainty for different components, and complex interactions between sources of uncertainty and error Complex, high dimension parameter space Bifurcations and discontinuous model changes High performance computational issues | View group |
| Approximating Computationally Intensive Functions and Sampling Design in High Dimensions - Methodology UQ | This is a core problem in constructing surrogates, with application to uncertainty propagation, inference, prediction, and design. Relevant research questions: Cross-examination of different methods: Projection, regression, interpolation, L1 minimization, Gaussian process/kriging, etc Appropriate measures of performance/accuracy and their dependence on the intended use of the surrogate model. Error analysis and convergence properties Sparse representations: l1 minimization, pursuit algorithms, low-rank approximation “Optimal” choices of nodes/design points for different methods Adaptive approaches: a posteriori error estimates; derivative properties; dimension reduction; additional optimization of nodes; ANOVA; relation to sequential design of experiments Interpreting and combining uncertainty information from stochastic surrogates (e.g., Gaussian process variance) and deterministic error bounds Deriving optimality criteria and search algorithms that are good for high dimensions Borrow existing theoretical results in high dimensional statistics (Donoho, etc.) to shed light on the structure of “optimal” designs in high dimension. Additional issues— Lack of regularity: discovering and approximating discontinuities in high dimensions Incorporating gradient information Enforcing constraints on output and input domains Fault tolerance and missing samples | View group |
| Inverse Function-based Inference - Methodology UQ | Jan Hannig (lead), D. Estep, Troy Butler (U. Texas), Simon Tavener (CSU) The working group will study the use of set-valued inversion of models for inference Research issues: Approximation of set-valued inverses in complex spaces Computation of inverse measures in parameter space Convergence and accuracy of computed inverse measures Theoretical issues regarding inversion of multiple observations Relation to fiducial inference and Dempster and Shafer calculus Intrusive and non-intrusive algorithms, dimension-benign computational algorithms | View group |
| Surrogate Models - Methodology UQ | This working group focuses on the exploration of properties, utility, and performance of two classes of model surrogates, namely Polynomial Chaos and Gaussian Process surrogates. The study will be done in the context of specific model problems with a range of difficulty involving nonlinearity and dimensionality. Test problems will include both algebraic functions as well as simple ODE/PDE problems. | View group |
| Engineered Systems - Engineering UQ | View group | |
| Sustainability - Engineering UQ | View group | |
| Materials - Engineering UQ | View group | |
| Renewable Energy - Engineering UQ | The group will consider uncertainty quantification issues arising in specific applications linked to renewable energy. In particular, we will study biofuels and wind farms. Other aspects involves the inclusion, in a UQ framework, of factors such as technological advances and/or regulations. | View group |
| Nuclear Energy - Engineering UQ | View group | |
| Geosciences - Geosciences UQ | View group | |
| Data Assimilation in IPCC Level Models - Climate UQ | In the IPCC AR4, the results were based on runs from ca. 24 models. These were built and run at climate research centers around the world and are each integrated Earth system models that comprise many components, including atmosphere, ocean, ice and land. The so-called dynamical core of such models is a computational model covering both the atmosphere and ocean and based on the primitive equations of GFD. While this is essentially computational, data come into the process of forming the final model. This incorporation occurs at a number of stages of the model development, including parametrization of sub-grid scale effects and model tuning. The process is not, however, done systematically and current practice is not thought of as "data assimilation." There seems to be a growing realization that DA will have a significant role to play in future climate model development. This is, in part, driven by the need to quantify uncertainty in the model predictions. Nevertheless, there is not a consensus as to how DA should be used in these large-scale climate models. This working group will consider the issues involved in formulating a plan for DA in such models. The first step will be to understand how such a model is put together and uncover all the steps where data is currently used in the model formation. For this purpose, we will look at the latest CESM from NCAR. | View group |
| Numerical Methods for Uncertainty Quantification - Spring Part 2 | Principal Instructors: Various Course Day and Time: Course will be held at SAMSI (driving directions) in RTP on Wednesdays, 4:30-7:00 p.m. in Room 150. Schedule: First class Wednesday, January 18, 2012 ; last class Wednesday, April 25, 2012 This course focuses on numerical methods for stochastic computation and uncertainty quantification (UQ). It is a two-semester course, where the first semester focuses on fundamental materials for UQ computing and the second semester on more advanced research materials. The main topics covered in the second semester include advanced numerical techniques for SPDE: adaptive methods and compressive sensing propagation of probability distributions Bayesian inference data assimilation model calibration Prerequisites: Numerical linear algebra, numerical methods for ordinary and partial differential equations; programming skill in one language, e.g., C/C++, FORTRAN, or Matlab. The course is open to all levels of graduate students in Mathematics, Statistics, as well as to those in other departments of sciences and engineering. Senior level undergraduate students with outstanding background are also considered. Registration for this course is being processed through your university. Duke: STA 294-01 NCSU: MA 810.002 UNC: STOR 891-001 Questions about the course or the UQ program should be emailed to uq@samsi.info. | View group |
2010
| Working Group | Description | Join |
|---|---|---|
| Dynamics OF Networks | Complex Networks Program working group. The dynamics of networks working group is exploring a variety of mathematical and statistical approaches for describing and understanding the changing connection topology of networks over time, the interplay of these network dynamics with other dynamic processes on the network, and the connections between these different mathematical and statistical methodologies. | View group |
| Sampling / Modeling / Inference | CN Program working group. The Working Group on Sampling/Modeling/Inference in networks aims to work towards moving the current state of knowledge on these inter-related tasks -- in the specific context of networks -- to rest on a more principled and integrated mathematical and statistical foundation. We are pursuing this goal by focusing on a handful of specific prototype problems in the context of certain application areas, ranging from information networks to animal communities to neuroscience. | View group |
| Dynamics ON Networks | CN Program working group. Random graphs are useful models of social and technological networks. To date most of the research in this area has concerned geometric properties of the graphs. This working group will focus on processes taking place ON the network. In particular we are interested in how their behavior on networks differs from that in homogeneously mixing populations or on regular lattices of the type commonly used in ecology and physics. | View group |
| Geometrical / Spectral Analysis | CN Program working group This working group is concerned with the following topics: detection of communities in networks, multiscale spectral methods for the analysis of the geometry of networks, algorithms that simplify graphs into simpler graphs in order to speed up certain optimization problems, metrics for comparing graphs, and multiscale homogenization of random walks. These topics have applications biology and to spread of "epidemics" in financial networks. | View group |
| Modeling Flows | CN Program working group. In this working group, we have formed two subgroups: (i) modeling traffic flows and (ii) modeling smart grid networks. In the traffic flows subgroup, people are interested in both stochastic and deterministic models to represent microscopic and macroscopic behavior of traffic flows. In modeling smart grid networks, the subgroup is working on mathematically formulating a graph/topology reduction problem of representing the larger network to a reduced number of local area networks, then modeling the corresponding dynamics and parameter estimation for the reduced system | View group |
| Hierarchical Methods for Object Data | AOOD Program working group. This working group is interested in developing hierarchical modeling approaches for object data, including functions, images, and more general structures like shapes and trees. The goal is to develop inferential methodology motivated by specific applications yielding complex, structured data. The idea of hierarchical modeling implies flexible, unified models that can simultaneously take into account variability and structure from multiple sources in the data set, within and between objects, and induced by the design or other measured covariates. Both Bayesian and frequentist approaches will be considered, and discussion of the connections and distinctions among existing Bayesian and frequentist approaches in the literature will be encouraged. | View group |
| Data Analysis on Sample Spaces with a Manifold Stratification | AOOD Program working group. Many statistics problems deal with data naturally sampled from spaces with singularities. For example, such is the case for shape data in three dimensions, where the sample space is a singular orbifold, or metric tree data, where the sample space is a polyhedral complex. Our goals are to understand the role of intrinsic and extrinsic geometry near singularities in asymptotics and to develop nonparametric methodologies and fast inference techniques in applications. | View group |
| Metrics on Shape Spaces | AOOD Program working group Principal Component Analysis depends on two metrics, a sample-based metric specified by a sample covariance matrix and a base metric usually specified by the identity matrix. In Functional Data Analysis, the base metric is often specified in terms of a differential operator to ensure a certain level of smoothness. | View group |
| Geometric Correspondence | AOOD Program working group. We are dealing with the alignment of objects (functions, landmark configurations, contours) using statistical methods. Applications in mind are brain imaging, face recognition, protein folding, etc. | View group |
| Dynamics and Inference | AOOD Program working group. The Dynamics and Inference group focuses on expanding inferential methodology for dynamic systems. Focus topics will be in inference about qualitative features of dynamical behavior, model selection and experimental design. | View group |
| Trees | AOOD Program working group. This SAMSI working group is engaged in collaborative research, on the development of new methodologies for the statistical analysis of populations of tree-structured data objects. Past approaches to this challenge have been diverse, and have been roughly categorized by our group as falling into the groups (i) Combinatorial, (ii) Folded Euclidean (maybe best known in the study of Phylogenetic Trees), (iii) Dyck Path. While research will continue in all of these areas, this SAMSI working group has begun bringing special value added in terms of cross-fertilization of ideas between these approaches. In addition, this group is benefiting from the larger SAMSI AOOD context through exploration of how classical (and thus much better developed) methods from shape and manifold data can inform the development of statistical techniques for handling trees. | View group |
| Brain Imaging | AOOD Program working group This group is exploring the role that Functional Data Analysis (statistical methods for samples of 'curves' or 'images') can play in the analysis of brain imaging (MRI, fMRI, PET, E/MEG) data. We focus on implementing theoretically rigorous methods for dependent high-dimensioanl functional data into fast analysis tools which are required to help provide answers to neuroscientific questions. | View group |
| Statistical Inference for Functional Data | AOOD Program working group This SAMSI working group focus on the development of new methodologies for several important problems in functional data analysis: (i) model selection for functional data; (ii) functional data clustering and classification; (iii) experiment design for functional data. In addition, the new methods will be validated and tested on practical applications involved with functional-featured data. | View group |