I³MS - Sayadi Seminar
Dr. Taraneh Sayadi - Optimization and Control of Complex Flows using Adjoint-Based Methods
Institut für technische Verbrennung, RWTH Aachen University
Numerical simulations of multiphysics and multiscale phenomena in fluid mechanics have advanced remarkably over the past decades. Complex physical processes, including among others multiphase flows, combustion and acoustics problems, and turbulent and thermal flows, can now be simulated with an astonishing degree of fidelity and accuracy. In spite of these advances, specifically with regards to more complex flows, modeling and simulation technologies remain at the stage of observation, reproduction, and prediction. However, optimization and control of such flows, by enhanced designs or active control strategies, is crucial for improvements in performance and robustness, and is necessary for venturing beyond standard operating conditions. The transition from model-based numerical solvers to model-based design and optimal control requires additional technology that enables relatively easy access to “inverse" information or backward solution. To date, this information has only been extracted from simulations of simplified configurations with additional unrealistic assumptions. In related fields (aero-dynamics, aero-acoustics), inverse optimization and control have improved airfoil shapes and reduced noise levels. Complex flows, including combustion or interfaces, however, constitute a far larger step in complexity, due to the presence of unsteadiness and nonlinearities, and therefore, require advanced techniques such as adjoint-based optimization. Throughout this talk we will first introduce an adjoint-based algorithm suitable for complex flow configurations, and then provide examples of reactive and interfacial flows where this algorithm has been implemented.
I³MS - Gatto Seminar
Dr. Paolo Gatto - Efficient Preconditioning of hp-FEM Matrices by Hierarchical Low-Rank Approximations
I³MS - Kalidindi Seminar
Prof. Dr. Surya Kalidindi - Materials Data Science and Informatics: A Key Enabler for Accelerated Materials Design, Development, and Deployment
Woodruff School of Mechanical Engineering, Georgia Institute of Technology, USA
The slow pace of new/improved materials development and deployment has been identified as the main bottleneck in the innovation cycles of most emerging technologies. The recent advances in data science can be leveraged suitably to address this impediment by effectively mediating between the seemingly disparate, inherently uncertain, multiscale and multimodal measurements and computations involved in the current materials development efforts. Proper utilization of modern data science in the materials development efforts can lead to a new generation of data-driven decision support tools for guiding effort investment (for both measurements and computations) at various stages of the materials development. It should also be recognized that the success of such ecosystems is predicated on the creation and utilization of integration platforms for promoting intimate, synchronous, collaborations between cross-disciplinary and distributed team members. This presentation provides a summary of recent advances made in our research group, and outlines specific directions of research that offer the most promising avenues.
EU Regional School - Ney Seminar
Prof. Dr. Hermann Ney - Human Language Technology and Machine Learning: From Bayes Decison Theory to Deep Learning
RWTH Aachen University
Spoken and written language and the processing of language are considered to be inherently human capabilities. With the advent of computing machinery, automatic language processing systems became one of the corner-stone goals in artificial intelligence. Typical tasks involve the recognition and understanding of speech, the recognition of text images and the translation between languages. The most successful approaches to building automatic systems to date are based on the idea that a computer learns from examples (possibly very large amounts) and uses plausibility scores rather than externally provided categorical rules. Such approaches are based on statistical decision theory and machine learning. The last 40 years have seen a dramatic progress in machine learning for human language technology. This lecture will present a unifying view of the underlying statistical methods including the recent developments in deep learning and artificial neural networks.
EU Regional School - Phillpot Seminar
Prof. Dr. Simon R. Phillpot - Classical Interatomic Potentials for Moilecular Dynamics Simulations: Recent Advances and Challenges
The remarkable increasing in computing power and rapid advances in simulation methodologies over the last three decades have led to computer simulation becoming a third approach, complementary to experiment and theory, to exploring materials systems. Molecular Dynamics (MD) simulation is the dominant method for the simulation of complex microstructures and dynamical effects with atomic-scale resolution.
This presentation offers a brief introduction to MD methods and then focuses in on the interatomic potential. The interatomic potential is a mathematical description of the interactions among the atoms and ions in the system and thus defines the material being simulated. To provide a foundation, we review some of the standard interatomic potentials for metals, ceramics and covalently-bonded materials. We then focus on recent developments in the area of reactive potentials that can describe materials systems in which metal, ionic and covalent bonding coexist. Specifically, we present details of the Charge Optimized Many Body (COMB) potential formalism and its applications to a number of materials systems. Finally, we address the issue of the development of interatomic potentials. Currently, this is a black art: it can take a skilled researcher many months to develop a potential for a single system. We present a new paradigm for the rational design of interatomic potentials that will greatly accelerate their development and offer a number of other advantages over standard approaches.