56 research outputs found
Deep learning applied to computational mechanics: A comprehensive review, state of the art, and the classics
Three recent breakthroughs due to AI in arts and science serve as motivation:
An award winning digital image, protein folding, fast matrix multiplication.
Many recent developments in artificial neural networks, particularly deep
learning (DL), applied and relevant to computational mechanics (solid, fluids,
finite-element technology) are reviewed in detail. Both hybrid and pure machine
learning (ML) methods are discussed. Hybrid methods combine traditional PDE
discretizations with ML methods either (1) to help model complex nonlinear
constitutive relations, (2) to nonlinearly reduce the model order for efficient
simulation (turbulence), or (3) to accelerate the simulation by predicting
certain components in the traditional integration methods. Here, methods (1)
and (2) relied on Long-Short-Term Memory (LSTM) architecture, with method (3)
relying on convolutional neural networks. Pure ML methods to solve (nonlinear)
PDEs are represented by Physics-Informed Neural network (PINN) methods, which
could be combined with attention mechanism to address discontinuous solutions.
Both LSTM and attention architectures, together with modern and generalized
classic optimizers to include stochasticity for DL networks, are extensively
reviewed. Kernel machines, including Gaussian processes, are provided to
sufficient depth for more advanced works such as shallow networks with infinite
width. Not only addressing experts, readers are assumed familiar with
computational mechanics, but not with DL, whose concepts and applications are
built up from the basics, aiming at bringing first-time learners quickly to the
forefront of research. History and limitations of AI are recounted and
discussed, with particular attention at pointing out misstatements or
misconceptions of the classics, even in well-known references. Positioning and
pointing control of a large-deformable beam is given as an example.Comment: 275 pages, 158 figures. Appeared online on 2023.03.01 at
CMES-Computer Modeling in Engineering & Science
Understanding the use of Virtual Reality in Marketing: a text mining-based review
The current study intends to highlight the most relevant studies in simulated realities with special attention to VR and marketing, showing how studies have evolved over time and discussing the findings. A text-mining approach using a Bayesian statistical topic model called latent Dirichlet allocation is employed to conduct a comprehensive analysis of 150 articles from 115 journals, all indexed in Web of Science. The findings reveal seven relevant topics, as well as the number of articles published over time, the authors most cited in VR papers and the leading journals in each topic. The article also provides theoretical and practical implications and suggestions for further research.info:eu-repo/semantics/acceptedVersio
The blessings of explainable AI in operations & maintenance of wind turbines
Wind turbines play an integral role in generating clean energy, but regularly suffer from operational inconsistencies and failures leading to unexpected downtimes and significant Operations & Maintenance (O&M) costs. Condition-Based Monitoring (CBM) has been utilised in the past to monitor operational inconsistencies in turbines by applying signal processing techniques to vibration data. The last decade has witnessed growing interest in leveraging Supervisory Control & Acquisition (SCADA) data from turbine sensors towards CBM. Machine Learning (ML) techniques have been utilised to predict incipient faults in turbines and forecast vital operational parameters with high accuracy by leveraging SCADA data and alarm logs. More recently, Deep Learning (DL) methods have outperformed conventional ML techniques, particularly for anomaly prediction. Despite demonstrating immense promise in transitioning to Artificial Intelligence (AI), such models are generally black-boxes that cannot provide rationales behind their predictions, hampering the ability of turbine operators to rely on automated decision making. We aim to help combat this challenge by providing a novel perspective on Explainable AI (XAI) for trustworthy decision support.This thesis revolves around three key strands of XAI – DL, Natural Language Generation (NLG) and Knowledge Graphs (KGs), which are investigated by utilising data from an operational turbine. We leverage DL and NLG to predict incipient faults and alarm events in the turbine in natural language as well as generate human-intelligible O&M strategies to assist engineers in fixing/averting the faults. We also propose specialised DL models which can predict causal relationships in SCADA features as well as quantify the importance of vital parameters leading to failures. The thesis finally culminates with an interactive Question- Answering (QA) system for automated reasoning that leverages multimodal domain-specific information from a KG, facilitating engineers to retrieve O&M strategies with natural language questions. By helping make turbines more reliable, we envisage wider adoption of wind energy sources towards tackling climate change
Biological Systems Workbook: Data modelling and simulations at molecular level
Nowadays, there are huge quantities of data surrounding the different fields of biology derived from experiments and theoretical simulations, where results are often stored in biological databases that are growing at a vertiginous rate every year. Therefore, there is an increasing research interest in the application of mathematical and physical models able to produce reliable predictions and explanations to understand and rationalize that information. All these investigations are helping to overcome biological questions pushing forward in the solution of problems faced by our society.
In this Biological Systems Workbook, we aim to introduce the basic pieces allowing life to take place, from the 3D structural point of view. We will start learning how to look at the 3D structure of molecules from studying small organic molecules used as drugs. Meanwhile, we will learn some methods that help us to generate models of these structures. Then we will move to more complex natural organic molecules as lipid or carbohydrates, learning how to estimate and reproduce their dynamics. Later, we will revise the structure of more complex macromolecules as proteins or DNA. Along this process, we will refer to different computational tools and databases that will help us to search, analyze and model the different molecular systems studied in this course
Bioinformatics
This book is divided into different research areas relevant in Bioinformatics such as biological networks, next generation sequencing, high performance computing, molecular modeling, structural bioinformatics, molecular modeling and intelligent data analysis. Each book section introduces the basic concepts and then explains its application to problems of great relevance, so both novice and expert readers can benefit from the information and research works presented here
Fundamental Approaches to Software Engineering
This open access book constitutes the proceedings of the 24th International Conference on Fundamental Approaches to Software Engineering, FASE 2021, which took place during March 27–April 1, 2021, and was held as part of the Joint Conferences on Theory and Practice of Software, ETAPS 2021. The conference was planned to take place in Luxembourg but changed to an online format due to the COVID-19 pandemic. The 16 full papers presented in this volume were carefully reviewed and selected from 52 submissions. The book also contains 4 Test-Comp contributions
Beyond rules: development and evaluation of knowledge acquisition systems for educational knowledge-based modelling
The technology of knowledge-based systems undoubtedly
offers potential for educational modelling, yet its practical impact
on today's school classrooms is very limited. To an extent this is
because the tools presently used in schools are EMYCIN -type
expert system shells. The main argument of this thesis is that
these shells make knowledge-based modelling unnecessarily
difficult and that tools which exploit knowledge acquisition
technologies empower learners to build better models. We
describe how such tools can be designed. To evaluate their
usability a model-building course was conducted in five secondary
schools. During the course pupils built hundreds of models in a
common range of domains. Some of the models were built with an
EMYCIN -type shell whilst others were built with a variety of
knowledge acquisition systems. The knowledge acquisition
systems emerged as superior in important respects. We offer some
explanations for these results and argue that although problems
remain, such as in teacher education, design of classroom practice,
and assessment of learning outcomes, it is clear that knowledge
acquisition systems offer considerable potential to develop
improved forms of educational knowledge-based modelling
COBE's search for structure in the Big Bang
The launch of Cosmic Background Explorer (COBE) and the definition of Earth Observing System (EOS) are two of the major events at NASA-Goddard. The three experiments contained in COBE (Differential Microwave Radiometer (DMR), Far Infrared Absolute Spectrophotometer (FIRAS), and Diffuse Infrared Background Experiment (DIRBE)) are very important in measuring the big bang. DMR measures the isotropy of the cosmic background (direction of the radiation). FIRAS looks at the spectrum over the whole sky, searching for deviations, and DIRBE operates in the infrared part of the spectrum gathering evidence of the earliest galaxy formation. By special techniques, the radiation coming from the solar system will be distinguished from that of extragalactic origin. Unique graphics will be used to represent the temperature of the emitting material. A cosmic event will be modeled of such importance that it will affect cosmological theory for generations to come. EOS will monitor changes in the Earth's geophysics during a whole solar color cycle
Safety and Reliability - Safe Societies in a Changing World
The contributions cover a wide range of methodologies and application areas for safety and reliability that contribute to safe societies in a changing world. These methodologies and applications include: - foundations of risk and reliability assessment and management
- mathematical methods in reliability and safety
- risk assessment
- risk management
- system reliability
- uncertainty analysis
- digitalization and big data
- prognostics and system health management
- occupational safety
- accident and incident modeling
- maintenance modeling and applications
- simulation for safety and reliability analysis
- dynamic risk and barrier management
- organizational factors and safety culture
- human factors and human reliability
- resilience engineering
- structural reliability
- natural hazards
- security
- economic analysis in risk managemen
- …