Conveners
Parallel Session: Tables and Images (PS1)
- Helene Hoffmann (FWCC)
Parallel Session: Surrogates and PINNs (PS2)
- There are no conveners in this block
Parallel Session: Simulations, Simulations (PS3)
- Peter Steinbach (HZDR)
Parallel Session: Reconstructions from Data (PS4)
- Patrick Stiller
In multiphase fluid dynamics knowledge of the particle size distribution of the dispersed phase is one of the key points of interest. In chemical engineering bubble columns are used as mass transfer apparatuses, where a gas is dispersed in a liquid phase. The size distribution of the bubbles is the determining factor for the total surface contact area between the two different phases and with...
The accurate modeling of materials is a fundamental task in material science. Advanced methods such as Density Functional Theory (DFT) provide quantum chemical accuracy through explicit calculation of the electronic structure of materials, but they come at high computational costs. These computational demands are especially prohibitive in the context of dynamic investigations. Increasingly...
The emerging advances in imaging technologies pave the way for the availability of a multitude of complementary data (e.g., spectral, spatial, elevation) in Earth sciences. Recently, hyperspectral imaging techniques have arisen as the most important tool to remotely acquire fine-spectral information from different materials/organisms. Nonetheless, such datasets require dedicated processing for...
This talk gives a brief introduction to uncertainty quantification (UQ) for neural networks. We investigate these methods as part of a Helmholtz AI voucher in collaboration with the MALA [1,2] project, where we build surrogate models to speed up demanding density functional theory calculations. In this context, UQ methods can be used to asses the validity of model predictions and can also...
Deep learning methods have found profound success in recent years in solving complex tasks such as in the field of computer vision, speech recognition, and security applications. The robustness of these deep learning models has been found to be vulnerable to adversarial examples. These are perturbed samples, which are imperceptible to the human eye, that lead the model to erroneous output...
In recent years, Physics Informed Neural Networks (PINNs) gained big traction in the scientific computing community. PINNs provide a neural-net-surrogate model parametrizing the solution space of a certain Partial Differential Equation (PDE) derived as the solution of a variational problem.
Thereby, the variational problem is typically formulated in terms of $L^2-$norms that are...
Numerical simulations of complex systems such as Laser-Plasma acceleration are computationally very expensive and have to be run on large-scale HPC systems. Offline analysis of experimental data is typically carried out by expensive grid scans or optimisation of particle-in-cell code like PIConGPU modelling the corresponding physical processes. Neural Network based surrogate models of this...
Helmholtz AI has been established to support scientists across the Helmholtz Society and across domains. The consultant team at HZDR was put in place to focus their support on matter research. That includes accelerator physics. Recently, we cooperated with DESY Hamburg on a voucher for the European XFEL.
To run experiments using the European XFEL it is essential that various laser systems...
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...
Solving partial differential equations (PDE) is an indispensable part of many branches of natural science. The analysis of experimental data by numerical simulation typically requires costly optimisation or grid-scan which is very costly. Machine Learning based surrogate models denote promising ways for fast approximation of these simulations by learning complex mapping from parameters to...
The understanding of laser-solid interactions is important to the development of future laser-driven particle and photon sources, e.g., for tumor therapy, astrophysics or fusion. Currently, these interactions can only be modeled by simulations which need verification in the real world. Consequently, in 2016, a pump-probe experiment was conducted by Thomas Kluge to examine the laser-plasma...
Radiation signatures emitted by Laser-plasma interactions are ubiquitous and are straightforward to experimentally acquire via imaging and spectroscopy. The data encodes phase-space dynamics on the smallest temporal and spatial scales. Yet such data is hard to interpret and thus is frequently discarded as being too complex. For theory and data analysis this raises several central questions:...
In this talk, I'd like to present modern machine learning tools for estimating the posterior of the inverse problem exposed in a beam control setting. That is, given an experimental beam profile, I'd like to demonstrate tools that help to estimate which simulation parameters might have been produced a similar beam profile with high likelihood.
