Multi-Information Source Fusion and Optimization to Realize ICME: Application to Dual Phase Materials

Integrated Computational Materials Engineering (ICME) calls for the integration of computational tools into the materials and parts development cycle, while the Materials Genome Initiative (MGI) calls for the acceleration of the materials development cycle through the combination of experiments, simulation, and data. As they stand, both ICME and MGI do not prescribe how to achieve the necessary tool integration or how to efficiently exploit the computational tools, in combination with experiments, to accelerate the development of new materials and materials systems. This paper addresses the first issue by putting forward a framework for the fusion of information that exploits correlations among sources/models and between the sources and `ground truth'. The second issue is addressed through a multi-information source optimization framework that identifies, given current knowledge, the next best information source to query and where in the input space to query it via a novel value-gradient policy. The querying decision takes into account the ability to learn correlations between information sources, the resource cost of querying an information source, and what a query is expected to provide in terms of improvement over the current state. The framework is demonstrated on the optimization of a dual-phase steel to maximize its strength-normalized strain hardening rate. The ground truth is represented by a microstructure-based finite element model while three low fidelity information sources---i.e. reduced order models---based on different homogenization assumptions---isostrain, isostress and isowork---are used to efficiently and optimally query the materials design space.Comment: 19 pages, 11 figures, 5 table

Information Theoretical Estimators Toolbox

We present ITE (information theoretical estimators) a free and open source, multi-platform, Matlab/Octave toolbox that is capable of estimating many different variants of entropy, mutual information, divergence, association measures, cross quantities, and kernels on distributions. Thanks to its highly modular design, ITE supports additionally (i) the combinations of the estimation techniques, (ii) the easy construction and embedding of novel information theoretical estimators, and (iii) their immediate application in information theoretical optimization problems. ITE also includes a prototype application in a central problem class of signal processing, independent subspace analysis and its extensions.Comment: 5 pages; ITE toolbox: https://bitbucket.org/szzoli/ite

Bayesian Optimization in Multi-Information Source and Large-Scale Systems

The advancements in science and technology in recent years have extended the scale of engineering problems. Discovery of new materials with desirable properties, drug discovery for treat-ment of disease, design of complex aerospace systems containing interactive subsystems, conducting experimental design of complex manufacturing processes, designing complex transportation systems all are examples of complex systems. The significant uncertainty and lack of knowledge about the underlying model due to the complexity necessitate the use of data for analyzing these systems. However, a huge time/economical expense involved in data gathering process avoids ac-quiring large amount of data for analyzing these systems. This dissertation is mainly focused on enabling design and decision making in complex uncertain systems. Design problems are pervasive in scientific and industrial endeavors: scientists design experiments to gain insights into physical and social phenomena, engineers design machines to execute tasks more efficiently, pharmaceutical researchers design new drugs to fight disease, and environ-mentalists design sensor networks to monitor ecological systems. All these design problems are fraught with choices, choices that are often complex and high-dimensional, with interactions that make them difficult for individuals to reason about. Bayesian optimization techniques have been successfully employed for experimental design of these complex systems. In many applications across computational science and engineering, engineers, scientists and decision-makers might have access to a system of interest through several models. These models, often referred to as “information sources", may encompass different resolutions, physics, and modeling assumptions, resulting in different “fidelity" or “skill" with respect to the quantities of interest. Examples of that include different finite-element models in design of complex mechanical structures, and various tools for analyzing DNA and protein sequence data in bioinformatics. Huge computation of the expensive models avoids excessive evaluations across design space. On the other hand, less expensive models fail to represent the objective function accurately. Thus, it is highly desirable to determine which experiment from which model should be conducted at each time point. We have developed a multi-information source Bayesian optimization framework capable of simultaneous selection of design input and information source, handling constraints, and making the balance between information gain and computational cost. The application of the proposed framework has been demonstrated on two different critical problems in engineering: 1) optimization of dual-phase steel to maximize its strength-normalized strain hardening rate in materials science; 2) optimization of NACA 0012 airfoil in aerospace. The design problems are often defined over a large input space, demanding large number of experiments for yielding a proper performance. This is not practical in many real-world problems, due to the budget limitation and data expenses. However, the objective function (i.e., experiment’s outcome) in many cases might not change with the same rate in various directions. We have introduced an adaptive dimensionality reduction Bayesian optimization framework that exponentially reduces the exploration region of the existing techniques. The proposed framework is capable of identifying a small subset of linear combinations of the design inputs that matter the most relative to the objective function and taking advantage of the objective function representation in this lower dimension, but with richer information. A significant increase in the rate of optimization process has been demonstrated on an important problem in aerospace regarding aerostructural design of an aircraft wing modeled based on the NASA Common Research Model (CRM)

Multi-hop Cooperative Relaying for Energy Efficient In Vivo Communications

This paper investigates cooperative relaying to support energy efficient in vivo communications. In such a network, the in vivo source nodes transmit their sensing information to an on-body destination node either via direct communications or by employing on-body cooperative relay nodes in order to promote energy efficiency. Two relay modes are investigated, namely single-hop and multi-hop (two-hop) relaying. In this context, the paper objective is to select the optimal transmission mode (direct, single-hop, or two-hop relaying) and relay assignment (if cooperative relaying is adopted) for each source node that results in the minimum per bit average energy consumption for the in vivo network. The problem is formulated as a binary program that can be efficiently solved using commercial optimization solvers. Numerical results demonstrate the significant improvement in energy consumption and quality-of-service (QoS) support when multi-hop communication is adopted

To Harvest and Jam: A Paradigm of Self-Sustaining Friendly Jammers for Secure AF Relaying

This paper studies the use of multi-antenna harvest-and-jam (HJ) helpers in a multi-antenna amplify-and-forward (AF) relay wiretap channel assuming that the direct link between the source and destination is broken. Our objective is to maximize the secrecy rate at the destination subject to the transmit power constraints of the AF relay and the HJ helpers. In the case of perfect channel state information (CSI), the joint optimization of the artificial noise (AN) covariance matrix for cooperative jamming and the AF beamforming matrix is studied using semi-definite relaxation (SDR) which is tight, while suboptimal solutions are also devised with lower complexity. For the imperfect CSI case, we provide the equivalent reformulation of the worst-case robust optimization to maximize the minimum achievable secrecy rate. Inspired by the optimal solution to the case of perfect CSI, a suboptimal robust scheme is proposed striking a good tradeoff between complexity and performance. Finally, numerical results for various settings are provided to evaluate the proposed schemes.Comment: 16 pages (double column), 8 figures, submitted for possible journal publicatio

Joint Source and Relay Precoding Designs for MIMO Two-Way Relaying Based on MSE Criterion

Properly designed precoders can significantly improve the spectral efficiency of multiple-input multiple-output (MIMO) relay systems. In this paper, we investigate joint source and relay precoding design based on the mean-square-error (MSE) criterion in MIMO two-way relay systems, where two multi-antenna source nodes exchange information via a multi-antenna amplify-and-forward relay node. This problem is non-convex and its optimal solution remains unsolved. Aiming to find an efficient way to solve the problem, we first decouple the primal problem into three tractable sub-problems, and then propose an iterative precoding design algorithm based on alternating optimization. The solution to each sub-problem is optimal and unique, thus the convergence of the iterative algorithm is guaranteed. Secondly, we propose a structured precoding design to lower the computational complexity. The proposed precoding structure is able to parallelize the channels in the multiple access (MAC) phase and broadcast (BC) phase. It thus reduces the precoding design to a simple power allocation problem. Lastly, for the special case where only a single data stream is transmitted from each source node, we present a source-antenna-selection (SAS) based precoding design algorithm. This algorithm selects only one antenna for transmission from each source and thus requires lower signalling overhead. Comprehensive simulation is conducted to evaluate the effectiveness of all the proposed precoding designs.Comment: 32 pages, 10 figure