Low rank surrogates for polymorphic fields with application to fuzzy-stochastic partial differential equations

We consider a general form of fuzzy-stochastic PDEs depending on the interaction of probabilistic and non-probabilistic ("possibilistic") influences. Such a combined modelling of aleatoric and epistemic uncertainties for instance can be applied beneficially in an engineering context for real-world applications, where probabilistic modelling and expert knowledge has to be accounted for. We examine existence and well-definedness of polymorphic PDEs in appropriate function spaces. The fuzzy-stochastic dependence is described in a high-dimensional parameter space, thus easily leading to an exponential complexity in practical computations. To aleviate this severe obstacle in practise, a compressed low-rank approximation of the problem formulation and the solution is derived. This is based on the Hierarchical Tucker format which is constructed with solution samples by a non-intrusive tensor reconstruction algorithm. The performance of the proposed model order reduction approach is demonstrated with two examples. One of these is the ubiquitous groundwater flow model with Karhunen-Loeve coefficient field which is generalized by a fuzzy correlation length

Low rank surrogates for polymorphic fields with application to fuzzy-stochastic partial differential equations

We consider a general form of fuzzy-stochastic PDEs depending on the interaction of probabilistic and non-probabilistic ("possibilistic") influences. Such a combined modelling of aleatoric and epistemic uncertainties for instance can be applied beneficially in an engineering context for real-world applications, where probabilistic modelling and expert knowledge has to be accounted for. We examine existence and well-definedness of polymorphic PDEs in appropriate function spaces. The fuzzy-stochastic dependence is described in a high-dimensional parameter space, thus easily leading to an exponential complexity in practical computations. To aleviate this severe obstacle in practise, a compressed low-rank approximation of the problem formulation and the solution is derived. This is based on the Hierarchical Tucker format which is constructed with solution samples by a non-intrusive tensor reconstruction algorithm. The performance of the proposed model order reduction approach is demonstrated with two examples. One of these is the ubiquitous groundwater flow model with Karhunen-Loeve coefficient field which is generalized by a fuzzy correlation length

A hybrid FETI-DP method for non-smooth random partial differential equations

A domain decomposition approach exploiting the localization of random parameters in high-dimensional random PDEs is presented. For high efficiency, surrogate models in multi-element representations are computed locally when possible. This makes use of a stochastic Galerkin FETI-DP formulation of the underlying problem with localized representations of involved input random fields. The local parameter space associated to a subdomain is explored by a subdivision into regions where the parametric surrogate accuracy can be trusted and where instead Monte Carlo sampling has to be employed. A heuristic adaptive algorithm carries out a problem-dependent hp refinement in a stochastic multi-element sense, enlarging the trusted surrogate region in local parametric space as far as possible. This results in an efficient global parameter to solution sampling scheme making use of local parametric smoothness exploration in the involved surrogate construction. Adequately structured problems for this scheme occur naturally when uncertainties are defined on sub-domains, e.g. in a multi-physics setting, or when the Karhunen-Loeve expansion of a random field can be localized. The efficiency of this hybrid technique is demonstrated with numerical benchmark problems illustrating the identification of trusted (possibly higher order) surrogate regions and non-trusted sampling regions

Towards a New Science of a Clinical Data Intelligence

In this paper we define Clinical Data Intelligence as the analysis of data generated in the clinical routine with the goal of improving patient care. We define a science of a Clinical Data Intelligence as a data analysis that permits the derivation of scientific, i.e., generalizable and reliable results. We argue that a science of a Clinical Data Intelligence is sensible in the context of a Big Data analysis, i.e., with data from many patients and with complete patient information. We discuss that Clinical Data Intelligence requires the joint efforts of knowledge engineering, information extraction (from textual and other unstructured data), and statistics and statistical machine learning. We describe some of our main results as conjectures and relate them to a recently funded research project involving two major German university hospitals.Comment: NIPS 2013 Workshop: Machine Learning for Clinical Data Analysis and Healthcare, 201

Mathematical Methods in Quantum Chemistry

The field of quantum chemistry is concerned with the analysis and simulation of chemical phenomena on the basis of the fundamental equations of quantum mechanics. Since the ‘exact’ electronic Schrödinger equation for a molecule with $N$ electrons is a partial differential equation in 3$N$ dimension, direct discretization of each coordinate direction into $K$ gridpoints yields $K^{3N}$ gridpoints. Thus a single Carbon atom ($N = 6$) on a coarse ten point grid in each direction ($K = 10$) already has a prohibitive $10^{18}$ degrees of freedom. Hence quantum chemical simulations require highly sophisticated model-reduction, approximation, and simulation techniques. The workshop brought together quantum chemists and the emerging and fast growing community of mathematicians working in the area, to assess recent advances and discuss long term prospects regarding the overarching challenges of (1) developing accurate reduced models at moderate computational cost, (2) developing more systematic ways to understand and exploit the multiscale nature of quantum chemistry problems. Topics of the workshop included: • wave function based electronic structure methods, • density functional theory, and • quantum molecular dynamics. Within these central and well established areas of quantum chemistry, the workshop focused on recent conceptual ideas and (where available) emerging mathematical results

Structural Biology by NMR: Structure, Dynamics, and Interactions

The function of bio-macromolecules is determined by both their 3D structure and conformational dynamics. These molecules are inherently flexible systems displaying a broad range of dynamics on time-scales from picoseconds to seconds. Nuclear Magnetic Resonance (NMR) spectroscopy has emerged as the method of choice for studying both protein structure and dynamics in solution. Typically, NMR experiments are sensitive both to structural features and to dynamics, and hence the measured data contain information on both. Despite major progress in both experimental approaches and computational methods, obtaining a consistent view of structure and dynamics from experimental NMR data remains a challenge. Molecular dynamics simulations have emerged as an indispensable tool in the analysis of NMR data

Simulating cosmic structure formation with the GADGET-4 code

Numerical methods have become a powerful tool for research in astrophysics, but their utility depends critically on the availability of suitable simulation codes. This calls for continuous efforts in code development, which is necessitated also by the rapidly evolving technology underlying today's computing hardware. Here we discuss recent methodological progress in the GADGET code, which has been widely applied in cosmic structure formation over the past two decades. The new version offers improvements in force accuracy, in time-stepping, in adaptivity to a large dynamic range in timescales, in computational efficiency, and in parallel scalability through a special MPI/shared-memory parallelization and communication strategy, and a more-sophisticated domain decomposition algorithm. A manifestly momentum conserving fast multipole method (FMM) can be employed as an alternative to the one-sided TreePM gravity solver introduced in earlier versions. Two different flavours of smoothed particle hydrodynamics, a classic entropy-conserving formulation and a pressure-based approach, are supported for dealing with gaseous flows. The code is able to cope with very large problem sizes, thus allowing accurate predictions for cosmic structure formation in support of future precision tests of cosmology, and at the same time is well adapted to high dynamic range zoom-calculations with extreme variability of the particle number density in the simulated volume. The GADGET-4 code is publicly released to the community and contains infrastructure for on-the-fly group and substructure finding and tracking, as well as merger tree building, a simple model for radiative cooling and star formation, a high dynamic range power spectrum estimator, and an initial conditions generator based on second-order Lagrangian perturbation theory.Comment: 82 pages, 65 figures, accepted by MNRAS, for the code see https://wwwmpa.mpa-garching.mpg.de/gadget