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publications
NAM: Normalization-based Attention Module
Published in ImageNet PPF @ NeurIPS 2021, 2021
we propose a novel normalization-based attention module (NAM), which suppresses less salient weights. It applies a weight sparsity penalty to the attention modules, thus, making them more computational efficient while retaining similar performance.
Recommended citation: Liu, Y., Shao, Z., Teng, Y., Hoffmann N. (2021). NAM: Normalization-based Attention Module. ImageNet PPF @ NeurIPS 2021. https://arxiv.org/abs/2111.12419
Data-Driven Shadowgraph Simulation of a 3D Object
Published in Simulation with Deep Learning @ ICLR, 2021
We propose a deep neural network based surrogate model for a plasma shadowgraph - a technique for visualization of perturbations in a transparent medium. We are substituting the numerical code by a computationally cheaper projection based surrogate model that is able to approximate the electric fields at a given time without computing all preceding electric fields as required by numerical methods. This means that the projection based surrogate model allows to recover the solution of the governing 3D partial differential equation, 3D wave equation, at any point of a given compute domain and configuration without the need to run a full simulation. This model has shown a good quality of reconstruction in a problem of interpolation of data within a narrow range of simulation parameters and can be used for input data of large size.
Recommended citation: Willmann, A., Stiller, P., Debus, A., Irman, A., Pausch, R., Chang, Y.-Y.,Bussmann, M., Hoffmann, N. (2021). Data-Driven Shadowgraph Simulation of a 3D Object. Simulation with Deep Learning @ ICLR. https://arxiv.org/abs/2106.00317
Invertible Surrogate Models: Joint Surrogate Modelling and Reconstruction of Laser Wakefield Acceleration by Invertible Neural Networks
Published in Simulation with Deep Learning @ ICLR, 2021
We will be introducing invertible surrogate models that approximate complex forward simulation of the physics involved in laser plasma accelerators: iLWFA. The bijective design of the surrogate model also provides all means for reconstruction of experimentally acquired diagnostics. The quality of our invertible laser wakefield acceleration network will be verified on a large set of numerical LWFA simulations.
Recommended citation: Bethke, F., Pausch, R., Stiller, P., Debus, A., Bussmann, M., Hoffmann, N. (2021). Invertible Surrogate Models: Joint Surrogate Modelling and Reconstruction of Laser Wakefield Acceleration by Invertible Neural Networks. Simulation with Deep Learning @ ICLR. https://arxiv.org/abs/2106.00432
Investigating Brain Connectivity with Graph Neural Networks and GNNExplainer
Published in ICPR 2022, 2022
Interpretable EEG classification with Graph Neural Networks.
Recommended citation: Zhdanov, M., Steinmann, S., Hoffmann N. (2022). Investigating Brain Connectivity with Graph Neural Networks and GNNExplainer. ICPR 2022. https://arxiv.org/abs/2206.01930
Learning Electron Bunch Distribution along a FEL Beamline by Normalising Flows
Published in Machine Learning and the Physical Sciences @ NeurIPS 2022, 2022
Learning Electron Bunch Distribution along a FEL Beamline by Normalising Flows
Recommended citation: Willmann A.,Cabadag J.C.,Chang Y.-Y.,Pausch R.,Ghaith A.,Debus A.,Irman A., Bussmann M., Schramm U., Hoffmann N. (2022). Learning Electron Bunch Distribution along a FEL Beamline by Normalising Flows. Machine Learning and the Physical Sciences @ NeurIPS 2022. https://ml4physicalsciences.github.io/2022/files/NeurIPS_ML4PS_2022_98.pdf
Amortized Bayesian Inference of GISAXS Data with Normalizing Flows
Published in Machine Learning and the Physical Sciences @ NeurIPS 2022, 2022
Amortized Bayesian Inference of GISAXS Data with Normalizing Flows
Recommended citation: Zhdanov, M., Randolph, L., Kluge, T., Nakatsutsumi, M., Gutt, C., Ganeva, M., Hoffmann, N. (2022). Amortized Bayesian Inference of GISAXS Data with Normalizing Flows. Machine Learning and the Physical Sciences @ NeurIPS 2022. https://arxiv.org/abs/2210.01543
Continual learning autoencoder training for a particle-in-cell simulation via streaming
Published in Machine Learning and the Physical Sciences @ NeurIPS 2022, 2022
Continual learning autoencoder training for a particle-in-cell simulation via streaming
Recommended citation: Stiller P., Makdani V., Pöschel F. , Richard P., Debus A., Bussmann M., Hoffmann N. (2022). Continual learning autoencoder training for a particle-in-cell simulation via streaming. Machine Learning and the Physical Sciences workshop @ NeurIPS 2022. https://arxiv.org/abs/2211.04770
talks
Talk 1 on Relevant Topic in Your Field
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Conference Proceeding talk 3 on Relevant Topic in Your Field
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teaching
Teaching experience 1
Undergraduate course, University 1, Department, 2014
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Teaching experience 2
Workshop, University 1, Department, 2015
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