1,114 research outputs found
Application of projection-based interpolation algorithm for non-stationary problem
In this paper we present a solver for non-stationary problems using L2 projection and h-adaptations. The solver utilizes the Euler time integration scheme for time evolution mixed with the projection based interpolation techniques for solving the L2 projections problem at every time step. The solver is tested on the model problem of the heat transfer in L-shape domain. We show that our solver delivers linear computational cost at every time step
Machine Learning for Fluid Mechanics
The field of fluid mechanics is rapidly advancing, driven by unprecedented
volumes of data from field measurements, experiments and large-scale
simulations at multiple spatiotemporal scales. Machine learning offers a wealth
of techniques to extract information from data that could be translated into
knowledge about the underlying fluid mechanics. Moreover, machine learning
algorithms can augment domain knowledge and automate tasks related to flow
control and optimization. This article presents an overview of past history,
current developments, and emerging opportunities of machine learning for fluid
mechanics. It outlines fundamental machine learning methodologies and discusses
their uses for understanding, modeling, optimizing, and controlling fluid
flows. The strengths and limitations of these methods are addressed from the
perspective of scientific inquiry that considers data as an inherent part of
modeling, experimentation, and simulation. Machine learning provides a powerful
information processing framework that can enrich, and possibly even transform,
current lines of fluid mechanics research and industrial applications.Comment: To appear in the Annual Reviews of Fluid Mechanics, 202
Learning-based methods for planning and control of humanoid robots
Nowadays, humans and robots are more and more likely to coexist as time goes by. The anthropomorphic nature of humanoid robots facilitates physical human-robot interaction, and makes social human-robot interaction more natural. Moreover, it makes humanoids ideal candidates for many applications related to tasks and environments designed for humans.
No matter the application, an ubiquitous requirement for the humanoid is to possess proper locomotion skills. Despite long-lasting research, humanoid locomotion is still far from being a trivial task. A common approach to address humanoid locomotion consists in decomposing its complexity by means of a model-based hierarchical control architecture. To cope with computational constraints, simplified models for the humanoid are employed in some of the architectural layers. At the same time, the redundancy of the humanoid with respect to the locomotion task as well as the closeness of such a task to human locomotion suggest a data-driven approach to learn it directly from experience.
This thesis investigates the application of learning-based techniques to planning and control of humanoid locomotion. In particular, both deep reinforcement learning and deep supervised learning are considered to address humanoid locomotion tasks in a crescendo of complexity.
First, we employ deep reinforcement learning to study the spontaneous emergence of balancing and push recovery strategies for the humanoid, which represent essential prerequisites for more complex locomotion tasks.
Then, by making use of motion capture data collected from human subjects, we employ deep supervised learning to shape the robot walking trajectories towards an improved human-likeness.
The proposed approaches are validated on real and simulated humanoid robots. Specifically, on two versions of the iCub humanoid: iCub v2.7 and iCub v3
Reinforcement Learning for Adaptive Mesh Refinement
Large-scale finite element simulations of complex physical systems governed
by partial differential equations crucially depend on adaptive mesh refinement
(AMR) to allocate computational budget to regions where higher resolution is
required. Existing scalable AMR methods make heuristic refinement decisions
based on instantaneous error estimation and thus do not aim for long-term
optimality over an entire simulation. We propose a novel formulation of AMR as
a Markov decision process and apply deep reinforcement learning (RL) to train
refinement policies directly from simulation. AMR poses a new problem for RL in
that both the state dimension and available action set changes at every step,
which we solve by proposing new policy architectures with differing generality
and inductive bias. The model sizes of these policy architectures are
independent of the mesh size and hence scale to arbitrarily large and complex
simulations. We demonstrate in comprehensive experiments on static function
estimation and the advection of different fields that RL policies can be
competitive with a widely-used error estimator and generalize to larger, more
complex, and unseen test problems.Comment: 14 pages, 13 figure
Model Order Reduction
An increasing complexity of models used to predict real-world systems leads to the need for algorithms to replace complex models with far simpler ones, while preserving the accuracy of the predictions. This three-volume handbook covers methods as well as applications. This third volume focuses on applications in engineering, biomedical engineering, computational physics and computer science
HPCCP/CAS Workshop Proceedings 1998
This publication is a collection of extended abstracts of presentations given at the HPCCP/CAS (High Performance Computing and Communications Program/Computational Aerosciences Project) Workshop held on August 24-26, 1998, at NASA Ames Research Center, Moffett Field, California. The objective of the Workshop was to bring together the aerospace high performance computing community, consisting of airframe and propulsion companies, independent software vendors, university researchers, and government scientists and engineers. The Workshop was sponsored by the HPCCP Office at NASA Ames Research Center. The Workshop consisted of over 40 presentations, including an overview of NASA's High Performance Computing and Communications Program and the Computational Aerosciences Project; ten sessions of papers representative of the high performance computing research conducted within the Program by the aerospace industry, academia, NASA, and other government laboratories; two panel sessions; and a special presentation by Mr. James Bailey
Safety of Autonomous Cognitive-oriented Robots
Service robots shall very soon autonomously provide services in all spheres of life by executing demanding and complex tasks in dynamic, complex environments and by collaborating with human users. In order to push forward the understanding of the safety problem a novel classification of robot hazards is provided. The so-called object interaction hazards are derived which arise when environment objects interact with objects that are manipulated by a robot. Taking into account the current state-of-the-art, it can be stated that this denotes a novel problem area. However, it is already proposed the so-called dynamic risk assessment approach, which shall enable the robot to perceive the risk of current and upcoming situations. In order to realize such a risk-aware planning system for the first time, dynamic risk assessment is integrated within a cognitive architecture serving cognitive functions like anticipation, planning and learning. In this connection, action spaces (sets of possible upcoming situations) are dynamically anticipated assessed with regard to comprised risks. Though, (initial) knowledge about hazards is required in order to realize this. Therefore, a novel procedural model is developed for systematically generating a safety knowledge base. However, it can be assumed that the safety knowledge potentially lacks completeness. The application of AI-based approaches constitutes a noteworthy opportunity. For this reason, light is shed on strategically influential learning methods in safety-critical contexts.
Finally, this work describes the generation, integration, utilization, and maintenance of a system-internal safety knowledge base for dynamic risk assessment. It denotes an overall concept toward solving the advanced safety problem and confirms in principle the realization of a safe behavior of autonomous and intelligent systems.Sicherheit autonomer kognitivorientierter Roboter
Autonome mobile Serviceroboter sollen zukĂŒnftig selbststĂ€ndig Dienstleistungen in allen Lebensbereichen erbringen, auch in direkter NĂ€he zum Menschen. Um das VerstĂ€ndnis fĂŒr Sicherheit in der Robotik zu erwei-tern, wird zunĂ€chst eine neue Klassifizierung der möglichen Gefahren vorgenommen. Hiervon wird die Klasse der Objektinteraktionsgefahren abgeleitet. Diese Gefahren entstehen, wenn Objekte der Umgebung mit denen interagieren, die der Roboter greift und transportiert. In Anbetracht des aktuellen Standes der Sicherheits-technik in der Robotik wird klar, dass sich hier ein neues Problemfeld auftut. GrundsĂ€tzlich wurde bereits ein dynamischer Risikountersuchungsansatz vorgeschlagen, welcher den Roboter selbst befĂ€higen soll, Situatio-nen hinsichtlich möglicher Gefahren zu untersuchen. Um dadurch eine risikobewusste Handlungsplanung erstmals zu realisieren, wird dieser in eine kognitive Architektur integriert, um kognitive Funktionen, wie Anti-zipation, Planen und Lernen zu nutzen. Hierbei werden mögliche HandlungsrĂ€ume dynamisch antizipiert und mittels dynamischer Risikoanalyse auf mögliche Gefahren untersucht. Um (Objektinteraktions-) Gefahren mit Hilfe der dynamischer Risikountersuchung bestimmen zu können, bedarf es eines (initialen) Wissens ĂŒber mögliche Gefahren. Aus diesem Grund wird ein Vorgehensmodell zur systematischen Erzeugung einer solchen Sicherheitswissensbasis entwickelt. Dieses Sicherheitswissen ist jedoch potentiell unvollstĂ€ndig. Daher stellt die Erweiterung und Verfeinerung desselben eine Notwendigkeit dar. Hierbei können die AnsĂ€tze aus dem Bereich der kĂŒnstlichen Intelligenz als nĂŒtzliche Möglichkeit wahrgenommen werden. Daher werden strate-gisch wichtige Lernmethoden hinsichtlich der Anwendung in einem sicherheitskritischen Kontext untersucht.
Die vorliegende Arbeit beschreibt die Erzeugung, die Integration, die Verwendung und die Aufrechterhaltung einer systeminternen Sicherheitswissensbasis zum Zwecke der dynamischen Risikountersuchung. Sie stellt hierbei ein Gesamtkonzept dar, dass zur Lösung des erweiterten Sicherheitsproblems beitrÀgt und somit die prinzipielle Realisierung des sicheren Betriebs von autonomen und intelligenten bestÀtigt
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