Zobrazeno 1 - 10
of 102
pro vyhledávání: '"Hoffmann, Nico"'
Autor:
Eberlein, Matthias, Hildebrand, Raphael, Tetzlaff, Ronald, Hoffmann, Nico, Kuhlmann, Levin, Brinkmann, Benjamin, Müller, Jens
Epilepsy is the most common neurological disorder and an accurate forecast of seizures would help to overcome the patient’s uncertainty and helplessness. In this contribution, we present and discuss a novel methodology for the classification of int
Externí odkaz:
https://tud.qucosa.de/id/qucosa%3A33336
https://tud.qucosa.de/api/qucosa%3A33336/attachment/ATT-0/
https://tud.qucosa.de/api/qucosa%3A33336/attachment/ATT-0/
Autor:
Ramanaik, Chethan Krishnamurthy, Cardona, Juan-Esteban Suarez, Willmann, Anna, Hanfeld, Pia, Hoffmann, Nico, Hecht, Michael
We formulate a data independent latent space regularisation constraint for general unsupervised autoencoders. The regularisation rests on sampling the autoencoder Jacobian in Legendre nodes, being the centre of the Gauss-Legendre quadrature. Revisiti
Externí odkaz:
http://arxiv.org/abs/2309.08228
Autor:
Weber, Dieter, Ehrig, Simeon, Schropp, Andreas, Clausen, Alexander, Achilles, Silvio, Hoffmann, Nico, Bussmann, Michael, Dunin-Borkowski, Rafal, Schroer, Christian G.
We demonstrate live-updating ptychographic reconstruction with ePIE, an iterative ptychography method, during ongoing data acquisition. The reconstruction starts with a small subset of the total data, and as the acquisition proceeds the data used for
Externí odkaz:
http://arxiv.org/abs/2308.10674
Autor:
Hoffmann, Nico, Drache, Georg, Koch, Edmund, Steiner, Gerald, Kirsch, Matthias, Petersohn, Uwe
Thermal imaging is a non-invasive and marker-free approach for intraoperative measurements of small temperature variations. In this work, we demonstrate the abilities of active dynamic thermal imaging for analysis of tissue perfusion state in case of
Externí odkaz:
http://nbn-resolving.de/urn:nbn:de:bsz:14-qucosa-230833
http://www.qucosa.de/fileadmin/data/qucosa/documents/23083/article_a2.pdf
http://www.qucosa.de/fileadmin/data/qucosa/documents/23083/article_a2.pdf
Autor:
Willmann, Anna, Cabadağ, Jurjen Couperus, Chang, Yen-Yu, Pausch, Richard, Ghaith, Amin, Debus, Alexander, Irman, Arie, Bussmann, Michael, Schramm, Ulrich, Hoffmann, Nico
Publikováno v:
Machine Learning and the Physical Sciences 2022 workshop, NeurIPS
Understanding and control of Laser-driven Free Electron Lasers remain to be difficult problems that require highly intensive experimental and theoretical research. The gap between simulated and experimentally collected data might complicate studies a
Externí odkaz:
http://arxiv.org/abs/2303.00657
Publikováno v:
DLDE Workshop in the 36th Conference on Neural Information Processing Systems (NeurIPS 2022)
Modelling the temperature of Electric Vehicle (EV) batteries is a fundamental task of EV manufacturing. Extreme temperatures in the battery packs can affect their longevity and power output. Although theoretical models exist for describing heat trans
Externí odkaz:
http://arxiv.org/abs/2212.08403
While the interaction of ultra-intense ultra-short laser pulses with near- and overcritical plasmas cannot be directly observed, experimentally accessible quantities (observables) often only indirectly give information about the underlying plasma dyn
Externí odkaz:
http://arxiv.org/abs/2212.05836
Steerable convolutional neural networks (CNNs) provide a general framework for building neural networks equivariant to translations and transformations of an origin-preserving group $G$, such as reflections and rotations. They rely on standard convol
Externí odkaz:
http://arxiv.org/abs/2212.06096
Autor:
Stiller, Patrick, Makdani, Varun, Pöschel, Franz, Pausch, Richard, Debus, Alexander, Bussmann, Michael, Hoffmann, Nico
The upcoming exascale era will provide a new generation of physics simulations. These simulations will have a high spatiotemporal resolution, which will impact the training of machine learning models since storing a high amount of simulation data on
Externí odkaz:
http://arxiv.org/abs/2211.04770
We demonstrate the utility of physics-informed neural networks (PINNs) as solvers for the non-relativistic, time-dependent Schr\"odinger equation. We study the performance and generalisability of PINN solvers on the time evolution of a quantum harmon
Externí odkaz:
http://arxiv.org/abs/2210.12522