A Unification of Ensemble Square Root Kalman Filters

In recent years, several ensemble-based Kalman filter algorithms have been developed that have been classified as ensemble square-root Kalman filters. Parallel to this development, the SEIK (Singular ``Evolutive'' Interpolated Kalman) filter has been introduced and applied in several studies. Some publications note that the SEIK filter is an ensemble Kalman filter or even an ensemble square-root Kalman filter. This study examines the relation of the SEIK filter to ensemble square-root filters in detail. It shows that the SEIK filter is indeed an ensemble-square root Kalman filter. Furthermore, a variant of the SEIK filter, the Error Subspace Transform Kalman Filter (ESTKF), is presented that results in identical ensemble transformations to those of the Ensemble Transform Kalman Filter (ETKF) while having a slightly lower computational cost. Numerical experiments are conducted to compare the performance of three filters (SEIK, ETKF, and ESTKF) using deterministic and random ensemble transformations. The results show better performance for the ETKF and ESTKF methods over the SEIK filter as long as this filter is not applied with a symmetric square root. The findings unify the separate developments that have been performed for the SEIK filter and the other ensemble square-root Kalman filters

Operational tsunami modelling with TsunAWI – recent developments and applications

In this article, the tsunami model TsunAWI (Alfred Wegener Institute) and its application for hindcasts, inundation studies, and the operation of the tsunami scenario repository for the Indonesian tsunami early warning system are presented. TsunAWI was developed in the framework of the German-Indonesian Tsunami Early Warning System (GITEWS) and simulates all stages of a tsunami from the origin and the propagation in the ocean to the arrival at the coast and the inundation on land. It solves the non-linear shallow water equations on an unstructured finite element grid that allows to change the resolution seamlessly between a coarse grid in the deep ocean and a fine representation of coastal structures. During the GITEWS project and the following maintenance phase, TsunAWI and a framework of pre- and postprocessing routines was developed step by step to provide fast computation of enhanced model physics and to deliver high quality results

Assimilation of dynamic topography in a global model

Absolute dynamic topography, i.e. the difference between time dependent multi-mission altimetric sea surface height and one of the most recent GOCE and GRACE based geoids, is assimilated in a global ocean general circulation model. To this end we apply an ensemble based Kalman technique, the "Error Subspace Transform Kalman Filter" (ESTKF). Here we present an update of our work. First of all the geoid is improved over previous versions. The ocean model now includes better dynamics and full sea-ice ocean interactions and more realistic surface forcing. Finally the assimilation method is augmented by a fixed lag smoother technique. This smoother allows to significantly improve the model performance, most strikingly in the first adjustment phase

Inferring material properties from FRP processes via sim-to-real learning

Fiber reinforced polymers (FRP) provide favorable properties such as weight-specific strength and stiffness that are central for certain industries, such as aerospace or automotive manufacturing. Liquid composite molding (LCM) is a family of often employed, inexpensive, out-of-autoclave manufacturing techniques. Among them, resin transfer molding (RTM), offers a high degree of automation. Herein, textile preforms are saturated by a fluid polymer matrix in a closed mold.Both impregnation quality and level of fiber volume content are of crucial importance for the final part quality. We propose to simultaneously learn three major textile properties (fiber volume content and permeability in X and Y direction) presented as a three-dimensional map based on a sequence of camera images acquired in flow experiments and compare CNNs, ConvLSTMs, and Transformers. Moreover, we show how simulation-to-real transfer learning can improve a digital twin in FRP manufacturing, compared to simulation-only models and models based on sparse real data. The overall best metrics are: IOU 0.5031 and Accuracy 95.929 %, obtained by pretrained transformer models

Architecture and Advanced Electronics Pathways Toward Highly Adaptive Energy- Efficient Computing

With the explosion of the number of compute nodes, the bottleneck of future computing systems lies in the network architecture connecting the nodes. Addressing the bottleneck requires replacing current backplane-based network topologies. We propose to revolutionize computing electronics by realizing embedded optical waveguides for onboard networking and wireless chip-to-chip links at 200-GHz carrier frequency connecting neighboring boards in a rack. The control of novel rate-adaptive optical and mm-wave transceivers needs tight interlinking with the system software for runtime resource management