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aLL-i@MOveO is reliable technology partner, on data driven engineering and featured product design using design patterns setup @data cluster. We partner with businesses to develop technology and featured design tool, enabled @data cluster on artificial neural network architecture using machine learning algorithms.
aLL-i@MOveO applies, hybrid, surrogate and learning surrogate modelling, using multipoint design methodology, to design, featured product on recognised patterns classified at data cluster. aLL-i@MOveO empower design engineers, with accurate, reliable modelling featured through life cycles of product design, starting from early stages of life to end of product design life.
The code structure follows a modular approach, allowing to replace parts of the code by external algorithms, to develop propulsion plant model, trained, developed using big data, vast size of unclassified data generated through life cycles of design, in cloud processed during learning process, to develop and update design model, analysed, assessed and validated in this process, more accurate and in detail, by observing all aspects of design, embraced through life..
The principal contribution of us into this project can be Reliability-based Multi-Design Point Methodology, adopted unconventional approach of engaging the reliability prediction by artificial Neural network (ANN) surrogate models rather than the time-consuming Monte Carlo (MC) simulation.
This is a new hybrid algorithm is presented to integrate the pre-training technique into neural network training procedure in order to enhance the Artificial Neural Network performance.
Surrogate model optimisation can also be used to augment fuel loading pattern optimisation, generating competitive results and providing enormous computational cost reduction and thus permitting more investigation within a given computational budget.
Key feature of this algorithm is that no dataset neither mesh is required for training the neural network. Instead, only a subset of points randomly sampled from the time and space domains are needed to assess whether the neural network output fits the Partial Differential Equations, its initial condition and its boundary conditions.
Furthermore, the algorithm relies on well-known machine learning methods . These methods are usually implemented in standard machine learning libraries such as TensorFlow
In particular, this library also provides tools to automatically differentiate the neural network, making the computation of the network spatial and time derivatives straightforward.
Our approach is based on the extension of an arbitrary classical dynamical system to a quantum system. This approach is based on classical systems extension to quantum states. The proposed theory can be applied to analysis of multiple (including non- Hamiltonian) dissipative dynamical systems.
Developed methods and algorithms integrated in quantum simulators will allow us to solve a wide range of problems with scientific and practical significance
This situation is challenging than traditional design methods. Owing to the realization of multi-mission adaptability requires more complex mechanical structure, the candidates of future design
Data driven engineering using machine learning/ deep learning algorithms on artificial neural network architecture starts with the setting up of design goals and then explores innumerable possible permutations of solutions, while simultaneously considering all constraints and requirements for finding the best design configuration. The algorithm aims to execute the design cycle multiple times and only applicable and near optimal solutions are preserved for each iteration.
Benefits of using quantum hybrid models aero engine conceptual design is already an extremely complicated problem, which involves the interaction of each component and the coupling of multiple disciplines. When the existence of uncertainty factors cannot be ignored, solving this problem becomes more difficult. Therefore, the traditional conceptual design method is facing challenges and it is worthwhile devoting much effort to this. We have to bring computing solutions to a problem involving airframe loads, mass modelling and structural analysis. Our target should be preserving structural integrity while optimising weight. Weight optimisation is key to low operating costs and reduced environmental impact.
Structural integrity can be demonstrated by simulating key flight occurrences through life cycles of design, required by air worthiness regulations.
New reliability based multi design point methodologies, adopted using unconventional approaches of engaging reliability prediction by Artificial Neural Network, surrogate models, can also make use of new computational hardware such as neural chips and quantum annealers.
The key to improving computational efficiency is to avoid performing the time-consuming probabilistic analysis by thermodynamic-based aero engine simulation model. Artificial neural network (ANN) is utilized to establish surrogate models to predict the aero engine performance reliability under multiple flight conditions
engineer-i, is design engineering suite of algorithms, featured using machine learning and deep learning technologies on artificial neural network architecture , to build surrogate quantum models, from classical mechanical models, enabled on cloud/edge.
engineer-i, is design engineering suite of algorithms, featured using machine learning and deep learning technologies on artificial neural network architecture , to build surrogate quantum models, from classical mechanical models, enabled on cloud/edge.
aLL-i @MOveO, founded to serve innovative minds, to empower design engineers, by design intelligence enabled to be operating in cloud cluster. aLL-i @MOveO has developed engineer-i API , Design engineering suite of AI tool kit series, on design features scripted simply in python, to design, to develop, and produce innovative design model beyond imagination and knowledge of designers, in cloud platform, using design analytics and computing power of quantum and neural chips, in distributed cluster.
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