Teaching solid mechanics to artificial intelligence—a fast solver for heterogeneous materials

Abstract We propose a deep neural network (DNN) as a fast surrogate model for local stress calculations in inhomogeneous non-linear materials. We show that the DNN predicts the local stresses with 3.8% mean absolute percentage error (MAPE) for the case of heterogeneous elastic media and a mechanical contrast of up to factor of 1.5 among neighboring domains, while performing 103 times faster than spectral solvers. The DNN model proves suited for reproducing the stress distribution in geometries different from those used for training. In the case of elasto-plastic materials with up to 4 times mechanical contrast in yield stress among adjacent regions, the trained model simulates the micromechanics with a MAPE of 6.4% in one single forward evaluation of the network, without any iteration. The results reveal an efficient approach to solve non-linear mechanical problems, with an acceleration up to a factor of 8300 for elastic-plastic materials compared to typical solvers

Modeling of orographic precipitation events in South America to couple hydrological and atmospheric models; part 1: The simulation of rain with the Mesoscale Model GESIMA

Globalmodelle sind aufgrund ihres groben Gitters (60 x 60 km) nur unzureichend in der Lage kleinskalige Prozesse (orographische Niederschlagsverstärkung) in der Atmosphäre aufzulösen. Mit Mesoskalenmodellen z.B. dem GESIMA (5 x 5 km) können deshalb die physikalische Grundlagen der Atmosphäre (Wolken- und Niederschlagsbildung) besser studiert und eine Kopplung mit hydrologischen Abflussmodellen erprobt werden. Zukünftig sieht dieses Projekt genau das vor, wobei der erste Teil, die Arbeit mit dem meteorologische Modell hier vorgestellt werden soll. Starkniederschlagserreignisse sind vielerorts auf der Welt mit charakteristischen Wetterlagen verbunden, die quasi über Tage unverändert ergiebigen Regen produzieren. Initialisiert mit den lokalen Vertikalprofilen aus Radiosondendaten, produzieren das prognostische Mesoskalenmodell GESIMA und das diagnostische Niederschlagsberechungsverfahren (MAXRR) maximale Regenmengen vergleichbarer Größenordnung.Global models are insufficient to solve small scale atmospheric processes (e.g. orographic precipitation) due to their gross resolution (60 x 60 km). With mesoscale models e.g. the GESIMA (5 x 5 km), the physical fundamentals of the atmosphere (formation of precipitation and clouds) can better be studied and a coupling with hydrological models be tested through. This project plans exactly, as a first step, the work with the cited meteorological model. Heavy rainfall events are connected with characteristic weather conditions in many places in the world which produce invariably rain quasi over days. Initialized with the local vertical profiles from radiosonde data, the prediction model GESIMA and the diagnostic model MAXRR produced rain quantities of comparable order of magnitude

A Model-Based Framework for Simplified Collaboration of Legal and Software Experts in Data Protection Assessments

The protection of personal data has become an increasingly important issue. Legal norms focused on data protection, such as the GDPR, provide legally binding requirements for systems that process personal data. Article 25 of the GDPR refers to the obligation to Data Protection by Design and Default. This can be achieved by conducting DPLA of the system in the early stages of development and implementing data protection concepts where necessary. This ties in with Article 35, which refers to an obligation to conduct DPLA before the actual processing of data. To aid in conducting continuous DPLA during the design time of software systems, we propose a model-based collaboration framework. This framework not only aids in providing consistent views of the software system for legal experts and software architects but also simplifies communication between both parties. We discuss the overall goals and benefits of such a framework and go into detail about the processes that interact as part of the framework. We also try to align legal concepts with the processes and describe the continuous iterative development using the collaboration framework

On computational irreducibility and the predictability of complex physical systems

Using elementary cellular automata (CA) as an example, we show how to coarse-grain CA in all classes of Wolfram's classification. We find that computationally irreducible (CIR) physical processes can be predictable and even computationally reducible at a coarse-grained level of description. The resulting coarse-grained CA which we construct emulate the large-scale behavior of the original systems without accounting for small-scale details. At least one of the CA that can be coarse-grained is irreducible and known to be a universal Turing machine.Comment: 4 pages, 2 figures, to be published in PR

Evolution and stability of a magnetic vortex in small cylindrical ferromagnetic particle under applied field

The energy of a displaced magnetic vortex in a cylindrical particle made of isotropic ferromagnetic material (magnetic dot) is calculated taking into account the magnetic dipolar and the exchange interactions. Under the simplifying assumption of small dot thickness the closed-form expressions for the dot energy is written in a non-perturbative way as a function of the coordinate of the vortex center. Then, the process of losing the stability of the vortex under the influence of the externally applied magnetic field is considered. The field destabilizing the vortex as well as the field when the vortex energy is equal to the energy of a uniformly magnetized state are calculated and presented as a function of dot geometry. The results (containing no adjustable parameters) are compared to the recent experiment and are in good agreement.Comment: 4 pages, 2 figures, RevTe

Measurement of the branching ratio for beta-delayed alpha decay of 16N

While the 12C(a,g)16O reaction plays a central role in nuclear astrophysics, the cross section at energies relevant to hydrostatic helium burning is too small to be directly measured in the laboratory. The beta-delayed alpha spectrum of 16N can be used to constrain the extrapolation of the E1 component of the S-factor; however, with this approach the resulting S-factor becomes strongly correlated with the assumed beta-alpha branching ratio. We have remeasured the beta-alpha branching ratio by implanting 16N ions in a segmented Si detector and counting the number of beta-alpha decays relative to the number of implantations. Our result, 1.49(5)e-5, represents a 24% increase compared to the accepted value and implies an increase of 14% in the extrapolated S-factor

1) Design of new Ti-based biomaterials by using ab-initio simulations, FEM, and experiments 2) Detailed analysis of an indent

Motivation Theoretical methods Experimental methods Results Conclusion