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Herzog & de Meuron
More than any of their contemporaries, Swiss architects Jacques Herzog and Pierre de Meuron are challenging the boundaries between architecture and art. Natural History explores that challenge, examining how the work of this formidable pair has drawn upon the art of both past and present, and brought architecture into dialogue with the art of our time. Echoing an encyclopedia, this publication reflects the natural history museum structure of the exhibition which it accompanies, organized by the Canadian Centre for Architecture. Models and projects by Herzog & de Meuron, as well as by other artists, are structured around six thematic portfolios that suggest an evolutionary history of the architects' work: Appropriation & Reconstruction, Transformation & Alienation, Stacking & Compression, Imprints & Moulds, Interlocking Spaces, and Beauty & Atmosphere. Each section is introduced with a statement from Herzog, and more than 20 artists, scholars, and architects have contributed essays, including Carrie Asman, Georges Didi-Huberman, Kurt W. Forster, Boris Groys, Ulrike Meyer Stump, Peggy Phelan, Thomas Ruff, Rebecca Schneider, Adolf Max Vogt, and Jeff Wall.
Knowledge Guided Machine Learning
Given their tremendous success in commercial applications, machine learning (ML) models are increasingly being considered as alternatives to science-based models in many disciplines. Yet, these "black-box" ML models have found limited success due to their inability to work well in the presence of limited training data and generalize to unseen scenarios. As a result, there is a growing interest in the scientific community on creating a new generation of methods that integrate scientific knowledge in ML frameworks. This emerging field, called scientific knowledge-guided ML (KGML), seeks a distinct departure from existing "data-only" or "scientific knowledge-only" methods to use knowledge and d...
Complex Effects in Large Eddy Simulations
The field of Large Eddy Simulations is reaching a level of maturity that brings this approach to the mainstream of engineering computations, while it opens opportunities and challenges. The main objective of this volume is to bring together leading experts in presenting the state-of-the-art and emerging approaches for treating complex effects in LES. A common theme throughout is the role of LES in the context of multiscale modeling and simulation.
Direct and Large-Eddy Simulation VI
The sixth ERCOFTAC Workshop on ‘Direct and Large-Eddy Simulation’ (DLES-6) was held at the University of Poitiers from September 12-14, 2005. Following the tradition of previous workshops in the DLES-series, this edition has reflected the state-of-the-art of numerical simulation of transitional and turbulent flows and provided an active forum for discussion of recent developments in simulation techniques and understanding of flow physics.
An Introduction to Continuous-Time Stochastic Processes
This textbook, now in its fourth edition, offers a rigorous and self-contained introduction to the theory of continuous-time stochastic processes, stochastic integrals, and stochastic differential equations. Expertly balancing theory and applications, it features concrete examples of modeling real-world problems from biology, medicine, finance, and insurance using stochastic methods. No previous knowledge of stochastic processes is required. Unlike other books on stochastic methods that specialize in a specific field of applications, this volume examines the ways in which similar stochastic methods can be applied across different fields. Beginning with the fundamentals of probability, the au...
Modeling Tumor Vasculature
To profoundly understand biology and harness its intricacies for human benefit and the mitigation of human harm requires cross-disciplinary approaches that incorporate sophisticated computational and mathematical modeling techniques. These integrative strategies are essential to achieve rapid and significant progress in issues, in health and disease, which span molecular, cellular and tissue levels. The use of mathematical models to describe various aspects of tumor growth has a very long history, dating back over six decades. Recently, however, experimental and computational advances have improved our in the understanding of how processes act at multiple scales to mediate the development of...
Nanometer Structures
This volume is a researcher's reference handbook to the many aspects of nanometer structures. Although intended as a source for the serious researcher, novices will find a great deal of interesting content. The theories covered include nanostructured thin films, photonic bandgap structures, quantum dots, carbon nanotubes, atomistic techniques, nanomechanics, nanofluidics, and quantum information processing. Modeling and simulation research on these topics have now reached a stage of maturity.
Operations Research Proceedings 2011
This book contains a selection of refereed papers presented at the “International Conference on Operations Research (OR 2011)” which took place at the University of Zurich from August 30 to September 2, 2011. The conference was jointly organized by the German speaking OR societies from Austria (ÖGOR), Germany (GOR) and Switzerland (SVOR) under the patronage of SVOR. More than 840 scientists and students from over 50 countries attended OR 2011 and presented 620 papers in 16 parallel topical streams, as well as special award sessions. The conference was designed according to the understanding of Operations Research as an interdisciplinary science focusing on modeling complex socio-technical systems to gain insight into behavior under interventions by decision makers. Dealing with “organized complexity” lies in the core of OR and designing useful support systems to master the challenge of system management in complex environment is the ultimate goal of our professional societies. To this end, algorithmic techniques and system modeling are two fundamental competences which are also well-balanced in these proceedings.
Machine Learning Applications in Subsurface Energy Resource Management
The utilization of machine learning (ML) techniques to understand hidden patterns and build data-driven predictive models from complex multivariate datasets is rapidly increasing in many applied science and engineering disciplines, including geo-energy. Motivated by these developments, Machine Learning Applications in Subsurface Energy Resource Management presents a current snapshot of the state of the art and future outlook for ML applications to manage subsurface energy resources (e.g., oil and gas, geologic carbon sequestration, and geothermal energy). Covers ML applications across multiple application domains (reservoir characterization, drilling, production, reservoir modeling, and pred...
Multiscale Cancer Modeling
Cancer is a complex disease process that spans multiple scales in space and time. Driven by cutting-edge mathematical and computational techniques, in silico biology provides powerful tools to investigate the mechanistic relationships of genes, cells, and tissues. It enables the creation of experimentally testable hypotheses, the integration of dat
