Improved methods for fast system reliability analysis through machine-learning-based surrogate models

In the aftermath of a natural disaster, knowledge of the connectivity of different regions of infrastructure networks is crucial to post-event decision making. The specific problem of determining the probability that two nodes in an infrastructure network are disconnected given the edge failure probabilities is known as the two-terminal connectivity problem, a special case of the k-terminal reliability problem. Both problems are known to be computationally intractable for general infrastructure graphs as the network size grows large, which motivates the use of Monte Carlo techniques to estimate the failure probability. However, Monte Carlo techniques are slow to converge due to the large number of realizations of the infrastructure graph required, each of which requires a connectivity evaluation. To improve the computation efficiency of the Monte Carlo approach, this work develops a new framework where the connectivity evaluation is itself estimated with a machine-learning-based surrogate model. The framework is applied to networks with both uncorrelated uniform edge failure probability and correlated edge failure probability, and an extension to node clusters is also proposed. The method first uses spectral clustering to partition the network, and estimates the connectivity of these clusters using both a logistic regression and an AdaBoost classifier. Numerical experiments on a California gas distribution network demonstrate that using the surrogate model to determine cluster connectivity introduces less than five percent error and is two orders of magnitude faster than methods using an exact connectivity evaluation to estimate the probability of network failure through Monte Carlo simulations

Accelerated System-Level Seismic Risk Assessment of Bridge Transportation Networks through Artificial Neural Network-Based Surrogate Model

Featured Application Post-hazard flow capacity of the lifeline network and recovery strategy against natural disaster. In this study, an artificial neural network (ANN)-based surrogate model is proposed to evaluate the system-level seismic risk of bridge transportation networks efficiently. To estimate the performance of a network, total system travel time (TSTT) was introduced as a performance index, and an ANN-based surrogate model was incorporated to evaluate a high-dimensional network with probabilistic seismic hazard analysis (PSHA) efficiently. To generate training data, the damage states of bridge components were considered as the input training data, and TSTT was selected as output data. An actual bridge transportation network in South Korea was considered as the target network, and the entire network map was reconstructed based on geographic information system data to demonstrate the proposed method. For numerical analysis, the training data were generated based on epicenter location history. By using the surrogate model, the network performance was estimated for various earthquake magnitudes at the trained epicenter with significantly-reduced computational time cost. In addition, 20 historical epicenters were adopted to confirm the robustness of the epicenter. Therefore, it was concluded that the proposed ANN-based surrogate model could be used as an alternative for efficient system-level seismic risk assessment of high-dimensional bridge transportation networks

Reliability analysis for automobile engines: conditional inference trees

The reliability model with covariates for machinery parts has been extensively studied by the proportional hazards model (PHM) and its variants. However, it is not straightforward to provide business recommendations based on the results of the PHM. We use a novel method, namely the Conditional Inference Tree, to conduct the reliability analysis for the automobile engines data, provided by a UK fleet company. We find that the reliability of automobile engines is significantly related to the vehicle age, early failure, and repair history. Our tree-structured model can be easily interpreted, and tangible business recommendations are provided for the fleet management and maintenance

Exploration of Reaction Pathways and Chemical Transformation Networks

For the investigation of chemical reaction networks, the identification of all relevant intermediates and elementary reactions is mandatory. Many algorithmic approaches exist that perform explorations efficiently and automatedly. These approaches differ in their application range, the level of completeness of the exploration, as well as the amount of heuristics and human intervention required. Here, we describe and compare the different approaches based on these criteria. Future directions leveraging the strengths of chemical heuristics, human interaction, and physical rigor are discussed.Comment: 48 pages, 4 figure

Topology Optimization via Machine Learning and Deep Learning: A Review

Topology optimization (TO) is a method of deriving an optimal design that satisfies a given load and boundary conditions within a design domain. This method enables effective design without initial design, but has been limited in use due to high computational costs. At the same time, machine learning (ML) methodology including deep learning has made great progress in the 21st century, and accordingly, many studies have been conducted to enable effective and rapid optimization by applying ML to TO. Therefore, this study reviews and analyzes previous research on ML-based TO (MLTO). Two different perspectives of MLTO are used to review studies: (1) TO and (2) ML perspectives. The TO perspective addresses "why" to use ML for TO, while the ML perspective addresses "how" to apply ML to TO. In addition, the limitations of current MLTO research and future research directions are examined

Bayesian Active Learning for Personalization and Uncertainty Quantification in Cardiac Electrophysiological Model

Cardiacvascular disease is the top death causing disease worldwide. In recent years, high-fidelity personalized models of the heart have shown an increasing capability to supplement clinical cardiology for improved patient-specific diagnosis, prediction, and treatment planning. In addition, they have shown promise to improve scientific understanding of a variety of disease mechanisms. However, model personalization by estimating the patient-specific tissue properties that are in the form of parameters of a physiological model is challenging. This is because tissue properties, in general, cannot be directly measured and they need to be estimated from measurements that are indirectly related to them through a physiological model. Moreover, these unknown tissue properties are heterogeneous and spatially varying throughout the heart volume presenting a difficulty of high-dimensional (HD) estimation from indirect and limited measurement data. The challenge in model personalization, therefore, summarizes to solving an ill-posed inverse problem where the unknown parameters are HD and the forward model is complex with a non-linear and computationally expensive physiological model. In this dissertation, we address the above challenge with following contributions. First, to address the concern of a complex forward model, we propose the surrogate modeling of the complex target function containing the forward model – an objective function in deterministic estimation or a posterior probability density function in probabilistic estimation – by actively selecting a set of training samples and a Bayesian update of the prior over the target function. The efficient and accurate surrogate of the expensive target function obtained in this manner is then utilized to accelerate either deterministic or probabilistic parameter estimation. Next, within the framework of Bayesian active learning we enable active surrogate learning over a HD parameter space with two novel approaches: 1) a multi-scale optimization that can adaptively allocate higher resolution to heterogeneous tissue regions and lower resolution to homogeneous tissue regions; and 2) a generative model from low-dimensional (LD) latent code to HD tissue properties. Both of these approaches are independently developed and tested within a parameter optimization framework. Furthermore, we devise a novel method that utilizes the surrogate pdf learned on an estimated LD parameter space to improve the proposal distribution of Metropolis Hastings for an accelerated sampling of the exact posterior pdf. We evaluate the presented methods on estimating local tissue excitability of a cardiac electrophysiological model in both synthetic data experiments and real data experiments. Results demonstrate that the presented methods are able to improve the accuracy and efficiency in patient-specific model parameter estimation in comparison to the existing approaches used for model personalization

Accelerated Fatigue Reliability Analysis of Stiffened Sections Using Deep Learning

Fatigue is one of the main failure mechanisms in structures subjected to fluctuating loads such as bridges and ships. If inadequately designed for such loads, fatigue can be detrimental to the safety of the structure. When fatigue cracks reach a certain size, sudden fracture failure or yielding of the reduced section can occur. Accordingly, quantifying the critical crack size is essential for determining the reliability of fatigue critical structures under growing cracks. Failure Assessment Diagrams (FADs) can be used to determine the critical crack size or whether the state of the crack is acceptable or not at a particular instant in time. Due to the presence of uncertainties in loads, material properties and crack growth behavior, probabilistic analysis is essential to understand the fatigue performance of the structure over its service life. A time dependent reliability profile for the structure can be established to help schedule maintenance and repair activities. However, probabilistic analysis of crack growth under complex geometrical and loading conditions can be very expensive computationally. Deep learning is a useful tool that is used in this study to curtail this lengthy process by establishing multi-variate non-linear approximations for complex fatigue crack growth profiles. This study proposes a framework for establishing the fatigue reliability profiles of stiffened panels under uncertainty. Monte Carlo simulation is used to draw samples from relevant probabilistic parameters and establish the time dependent reliability profile of the structure under propagating cracks. Deep learning is adopted to improve the computational efficiency of the probabilistic analysis in establishing the probabilistic crack growth profiles. The proposed framework is illustrated on a bridge with stiffened tub girders subjected to fatigue loading.Civil Engineerin

Simulation Intelligence: Towards a New Generation of Scientific Methods

The original "Seven Motifs" set forth a roadmap of essential methods for the field of scientific computing, where a motif is an algorithmic method that captures a pattern of computation and data movement. We present the "Nine Motifs of Simulation Intelligence", a roadmap for the development and integration of the essential algorithms necessary for a merger of scientific computing, scientific simulation, and artificial intelligence. We call this merger simulation intelligence (SI), for short. We argue the motifs of simulation intelligence are interconnected and interdependent, much like the components within the layers of an operating system. Using this metaphor, we explore the nature of each layer of the simulation intelligence operating system stack (SI-stack) and the motifs therein: (1) Multi-physics and multi-scale modeling; (2) Surrogate modeling and emulation; (3) Simulation-based inference; (4) Causal modeling and inference; (5) Agent-based modeling; (6) Probabilistic programming; (7) Differentiable programming; (8) Open-ended optimization; (9) Machine programming. We believe coordinated efforts between motifs offers immense opportunity to accelerate scientific discovery, from solving inverse problems in synthetic biology and climate science, to directing nuclear energy experiments and predicting emergent behavior in socioeconomic settings. We elaborate on each layer of the SI-stack, detailing the state-of-art methods, presenting examples to highlight challenges and opportunities, and advocating for specific ways to advance the motifs and the synergies from their combinations. Advancing and integrating these technologies can enable a robust and efficient hypothesis-simulation-analysis type of scientific method, which we introduce with several use-cases for human-machine teaming and automated science