On-the-fly adaptivity for nonlinear twoscale simulations using artificial neural networks and reduced order modeling

A multi-fidelity surrogate model for highly nonlinear multiscale problems is proposed. It is based on the introduction of two different surrogate models and an adaptive on-the-fly switching. The two concurrent surrogates are built incrementally starting from a moderate set of evaluations of the full order model. Therefore, a reduced order model (ROM) is generated. Using a hybrid ROM-preconditioned FE solver, additional effective stress-strain data is simulated while the number of samples is kept to a moderate level by using a dedicated and physics-guided sampling technique. Machine learning (ML) is subsequently used to build the second surrogate by means of artificial neural networks (ANN). Different ANN architectures are explored and the features used as inputs of the ANN are fine tuned in order to improve the overall quality of the ML model. Additional ANN surrogates for the stress errors are generated. Therefore, conservative design guidelines for error surrogates are presented by adapting the loss functions of the ANN training in pure regression or pure classification settings. The error surrogates can be used as quality indicators in order to adaptively select the appropriate -- i.e. efficient yet accurate -- surrogate. Two strategies for the on-the-fly switching are investigated and a practicable and robust algorithm is proposed that eliminates relevant technical difficulties attributed to model switching. The provided algorithms and ANN design guidelines can easily be adopted for different problem settings and, thereby, they enable generalization of the used machine learning techniques for a wide range of applications. The resulting hybrid surrogate is employed in challenging multilevel FE simulations for a three-phase composite with pseudo-plastic micro-constituents. Numerical examples highlight the performance of the proposed approach

H-word: Supporting job scheduling in Hadoop with workload-driven data redistribution

The final publication is available at http://link.springer.com/chapter/10.1007/978-3-319-44039-2_21Today’s distributed data processing systems typically follow a query shipping approach and exploit data locality for reducing network traffic. In such systems the distribution of data over the cluster resources plays a significant role, and when skewed, it can harm the performance of executing applications. In this paper, we addressthe challenges of automatically adapting the distribution of data in a cluster to the workload imposed by the input applications. We propose a generic algorithm, named H-WorD, which, based on the estimated workload over resources, suggests alternative execution scenarios of tasks, and hence identifies required transfers of input data a priori, for timely bringing data close to the execution. We exemplify our algorithm in the context of MapReduce jobs in a Hadoop ecosystem. Finally, we evaluate our approach and demonstrate the performance gains of automatic data redistribution.Peer ReviewedPostprint (author's final draft

Joint On-the-Fly Network Coding/Video Quality Adaptation for Real-Time Delivery

This paper introduces a redundancy adaptation algorithm for an on-the-fly erasure network coding scheme called Tetrys in the context of real-time video transmission. The algorithm exploits the relationship between the redundancy ratio used by Tetrys and the gain or loss in encoding bit rate from changing a video quality parameter called the Quantization Parameter (QP). Our evaluations show that with equal or less bandwidth occupation, the video protected by Tetrys with redundancy adaptation algorithm obtains a PSNR gain up to or more 4 dB compared to the video without Tetrys protection. We demonstrate that the Tetrys redundancy adaptation algorithm performs well with the variations of both loss pattern and delay induced by the networks. We also show that Tetrys with the redundancy adaptation algorithm outperforms FEC with and without redundancy adaptation

Integrated reconfigurable control and guidance based on evaluation of degraded performance

The present paper is focused on analysing an integrated reconfigurable control and guidance approach for recovering a small fixed-wing UAV from different actuator faults, which cover locked in place (stuck) and loss of effectiveness. The model of the UAV Aerosonde is used to develop a reconfigurable control system based on the control allocation technique for a variety of faults, such as locked-in-place control surfaces. It is shown through simulation that the developed technique is successful to recover the aircraft from various faults but cannot guarantee success on the planned mission. For mission scenarios where performance degradation is such that the prescribed trajectory cannot be achieved, a reconfigurable guidance system is developed, which is capable of adapting parameters such as the minimum turning radius and the look-ahead distance for obstacle avoidance, to allow the vehicle to dynamically generate a path which guides the aircraft around the no-fly zones taking into account the post-fault reduced performance. Path following is performed by means of a non-linear lateral guidance law and a collision avoidance algorithm is implemented as well. Finally, the integration of control reconfiguration and guidance adaptation is carried out to maximise probabilities of post-failure success in the mission. A methodology is developed, using an error based control allocation parameter, as a measure of performance degradation, which links both reconfiguration and guidance systems. The developed method, although approximate, is proven to be an efficient way of allocating the required degree of reconfiguration in guidance commands when an accurate prediction of the actual performance is not available