@article{16290,
abstract = {The control of complex systems is of critical importance in many branches of science, engineering, and industry, many of which are governed by nonlinear partial differential equations. Controlling an unsteady fluid flow is particularly important, as flow control is a key enabler for technologies in energy (e.g., wind, tidal, and combustion), transportation (e.g., planes, trains, and automobiles), security (e.g., tracking airborne contamination), and health (e.g., artificial hearts and artificial respiration). However, the high-dimensional, nonlinear, and multi-scale dynamics make real-time feedback control infeasible. Fortunately, these high- dimensional systems exhibit dominant, low-dimensional patterns of activity that can be exploited for effective control in the sense that knowledge of the entire state of a system is not required. Advances in machine learning have the potential to revolutionize flow control given its ability to extract principled, low-rank feature spaces characterizing such complex systems.We present a novel deep learning modelpredictive control framework that exploits low-rank features of the flow in order to achieve considerable improvements to control performance. Instead of predicting the entire fluid state, we use a recurrent neural network (RNN) to accurately predict the control relevant quantities of the system, which are then embedded into an MPC framework to construct a feedback loop. In order to lower the data requirements and to improve the prediction accuracy and thus the control performance, incoming sensor data are used to update the RNN online. The results are validated using varying fluid flow examples of increasing complexity.},
author = {Bieker, Katharina and Peitz, Sebastian and Brunton, Steven L. and Kutz, J. Nathan and Dellnitz, Michael},
issn = {0935-4964},
journal = {Theoretical and Computational Fluid Dynamics},
pages = {577–591},
title = {{Deep model predictive flow control with limited sensor data and online learning}},
doi = {10.1007/s00162-020-00520-4},
volume = {34},
year = {2020},
}
@unpublished{19941,
abstract = {In backward error analysis, an approximate solution to an equation is
compared to the exact solution to a nearby "modified" equation. In numerical
ordinary differential equations, the two agree up to any power of the step
size. If the differential equation has a geometric property then the modified
equation may share it. In this way, known properties of differential equations
can be applied to the approximation. But for partial differential equations,
the known modified equations are of higher order, limiting applicability of the
theory. Therefore, we study symmetric solutions of discretized partial
differential equations that arise from a discrete variational principle. These
symmetric solutions obey infinite-dimensional functional equations. We show
that these equations admit second-order modified equations which are
Hamiltonian and also possess first-order Lagrangians in modified coordinates.
The modified equation and its associated structures are computed explicitly for
the case of rotating travelling waves in the nonlinear wave equation.},
author = {McLachlan, Robert I and Offen, Christian},
booktitle = {arXiv:2006.14172},
title = {{Backward error analysis for variational discretisations of partial differential equations}},
year = {2020},
}
@article{19939,
author = {Kreusser, Lisa Maria and McLachlan, Robert I and Offen, Christian},
issn = {0951-7715},
journal = {Nonlinearity},
number = {5},
pages = {2335--2363},
title = {{Detection of high codimensional bifurcations in variational PDEs}},
doi = {10.1088/1361-6544/ab7293},
volume = {33},
year = {2020},
}
@inproceedings{19953,
abstract = {Current GNN architectures use a vertex neighborhood aggregation scheme, which limits their discriminative power to that of the 1-dimensional Weisfeiler-Lehman (WL) graph isomorphism test. Here, we propose a novel graph convolution operator that is based on the 2-dimensional WL test. We formally show that the resulting 2-WL-GNN architecture is more discriminative than existing GNN approaches. This theoretical result is complemented by experimental studies using synthetic and real data. On multiple common graph classification benchmarks, we demonstrate that the proposed model is competitive with state-of-the-art graph kernels and GNNs.},
author = {Damke, Clemens and Melnikov, Vitaly and Hüllermeier, Eyke},
booktitle = {Proceedings of the 12th Asian Conference on Machine Learning (ACML 2020)},
editor = {Jialin Pan, Sinno and Sugiyama, Masashi},
keyword = {graph neural networks, Weisfeiler-Lehman test, cycle detection},
location = {Bangkok, Thailand},
pages = {49--64},
publisher = {PMLR},
title = {{A Novel Higher-order Weisfeiler-Lehman Graph Convolution}},
volume = {129},
year = {2020},
}
@inbook{20568,
author = {Reinhold, Jannik and Koldewey, Christian and Dumitrescu, Roman},
booktitle = {Der Geschäftsmodell-Toolguide },
editor = {Buchholz, Birgit and Bürger, Matthias},
pages = {52--56},
publisher = {Campus Verlag},
title = {{GEMINI-Modellierungssprache für Wertschöpfungssysteme}},
year = {2020},
}
@inbook{20570,
author = {Koldewey, Christian and Reinhold, Jannik and Dumitrescu, Roman},
booktitle = {Der Geschäftsmodell-Toolguide},
editor = {Buchholz, Birgit and Bürger, Matthias},
pages = {61--66},
publisher = {Campus Verlag},
title = {{GEMINI-Geschäftsmodellmuster-Kartenset}},
year = {2020},
}
@inproceedings{20695,
author = {Boeddeker, Christoph and Nakatani, Tomohiro and Kinoshita, Keisuke and Haeb-Umbach, Reinhold},
booktitle = {ICASSP 2020 - 2020 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)},
isbn = {9781509066315},
title = {{Jointly Optimal Dereverberation and Beamforming}},
doi = {10.1109/icassp40776.2020.9054393},
year = {2020},
}
@inproceedings{20753,
abstract = {In this paper we present our system for the detection and classification of acoustic scenes and events (DCASE) 2020 Challenge Task 4: Sound event detection and separation in domestic environments. We introduce two new models: the forward-backward convolutional recurrent neural network (FBCRNN) and the tag-conditioned convolutional neural network (CNN). The FBCRNN employs two recurrent neural network (RNN) classifiers sharing the same CNN for preprocessing. With one RNN processing a recording in forward direction and the other in backward direction, the two networks are trained to jointly predict audio tags, i.e., weak labels, at each time step within a recording, given that at each time step they have jointly processed the whole recording. The proposed training encourages the classifiers to tag events as soon as possible. Therefore, after training, the networks can be applied to shorter audio segments of, e.g., 200ms, allowing sound event detection (SED). Further, we propose a tag-conditioned CNN to complement SED. It is trained to predict strong labels while using (predicted) tags, i.e., weak labels, as additional input. For training pseudo strong labels from a FBCRNN ensemble are used. The presented system scored the fourth and third place in the systems and teams rankings, respectively. Subsequent improvements allow our system to even outperform the challenge baseline and winner systems in average by, respectively, 18.0% and 2.2% event-based F1-score on the validation set. Source code is publicly available at https://github.com/fgnt/pb_sed.},
author = {Ebbers, Janek and Haeb-Umbach, Reinhold},
booktitle = {Proceedings of the Detection and Classification of Acoustic Scenes and Events 2020 Workshop (DCASE2020)},
title = {{Forward-Backward Convolutional Recurrent Neural Networks and Tag-Conditioned Convolutional Neural Networks for Weakly Labeled Semi-Supervised Sound Event Detection}},
year = {2020},
}
@inproceedings{20854,
author = {Camberg, Alan Adam and Tröster, Thomas},
location = {Seoul, South Korea},
title = {{A simplified method for the evaluation of the layer compression test using one 3D digital image correlation system and considering the material anisotropy by the equibiaxial Lankford parameter}},
doi = {10.1088/1757-899X/967/1/012077},
year = {2020},
}
@misc{21294,
author = {Hagengruber, Ruth},
booktitle = {H-France Net },
issn = {ISSN 1553-9172},
title = {{Review Hagengruber Le Rue Émilie Du Châtelet Philosophe }},
volume = {158},
year = {2020},
}