Traditional design cycles for new materials and assemblies have two fundamental drawbacks. The underlying physical relationships are often too complex to be precisely calculated and described. Aside from that, many unknown uncertainties, such as exact manufacturing parameters or materials composition, dominate the real assembly behavior. Machine learning (ML) methods overcome these fundamental limitations through data-driven learning. In addition, modern approaches can specifically increase system knowledge. Representation Learning allows the physical, and if necessary, even symbolic interpretation of the learned solution. In this way, the most complex physical relationships can be considered and quickly described. Furthermore, generative ML approaches can synthesize possible morphologies of the materials based on defined conditions to visualize the effects of uncertainties. This modern approach accelerates the design process for new materials and enables the prediction and interpretation of realistic materials behavior.

But two main contradictions remain: First, AI/ML are very heterogeneous, and Services for Cyber-Physical Systems based on Artificial each AI/ML method comes with a specialized model Intelligence and Machine Learning require formalism to capture relevant aspects of the environment a virtual representation of the physical. To reduce and the application domain. Hence, the modeling efforts and to synchronize results, for each question is how a DigitalTwin can provide the correct system, a common and unique virtual representation model to each AI/ML method. The second used by all services during the whole system contradiction is that AI/ML requires explicit, i.e. life-cycle is needed--i.e. a DigitalTwin. In this paper by an algorithm processable knowledge, since compiled such a DigitalTwin, namely the AI reference knowledge in form of simulation libraries, raw model AITwin, is defined. This reference model is data or executables does not help. But most publications verified by using a running example from process refer to these kind of information.

NEW YORK, NY--(Marketwired - Jun 15, 2016) - Datorama, a global leader in marketing analytics innovation, today announced the company has added an office in Europe. The latest addition to Datorama's global footprint is located in Hamburg, Germany and marks a critical milestone as the company expands into the German, Austrian and Swiss (DACH) region. Datorama's Hamburg office further strengthens a robust EMEA presence, which includes: Amsterdam, Barcelona, London and Paris. Designed for marketers, Datorama's Marketing Integration Engine helps leading enterprises, agencies and publishers centralize all of their marketing data across silos for cross-channel visualization, analysis and data-driven insight generation. By analyzing inputs from unlimited data sources, including online and offline marketing channels, and first- and third-party applications across CRM, billing, call centers, and more, the company's patent-pending artificial intelligence (AI)-based software delivers a single source of truth at the data layer to drive tactical and strategic marketing performance optimization.

NEW YORK, NY--(Marketwired - Jun 15, 2016) - Datorama, a global leader in marketing analytics innovation, today announced the company has added an office in Europe. The latest addition to Datorama's global footprint is located in Hamburg, Germany and marks a critical milestone as the company expands into the German, Austrian and Swiss (DACH) region. Datorama's Hamburg office further strengthens a robust EMEA presence, which includes: Amsterdam, Barcelona, London and Paris. Designed for marketers, Datorama's Marketing Integration Engine helps leading enterprises, agencies and publishers centralize all of their marketing data across silos for cross-channel visualization, analysis and data-driven insight generation. By analyzing inputs from unlimited data sources, including online and offline marketing channels, and first- and third-party applications across CRM, billing, call centers, and more, the company's patent-pending artificial intelligence (AI)-based software delivers a single source of truth at the data layer to drive tactical and strategic marketing performance optimization.