Data driven engineering using machine learninging/ 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. ML algorithm  comprises data-driven optimization and applied regression techniques that are well suited for high-dimensional, nonlinear problems, such as those encountered in fluid dynamics, and structural dynamics. I have put  my years of engineering experience and knowledge on engineering modelling and simulations into product design  using  inverse methodology of data driven engineering , which empowers design engineer to design by  big data and design  features, generated in cloud cluster.

Our Story

An artificial neural network (ANN) is trained to generalize on characteristics of product concepts typically known in the conceptual design phase, and environmental data from pre-existing LCA. This is done without the overhead of defining new LCA models on a product-by-product basis.

The learning surrogate LCA method is appropriate to inform system design decision making in early conceptual stages of product development.

The approach facilitates an integrated system design process, allowing the approximate and rapid assessment of environmental impact based on high-level information typically known in the conceptual phase.

The learning process of the ANN begins when it is provided a set of product descriptors and corresponding detailed LCA results from previously analyzed existing products. The training algorithms adjust parameters within the network so that its output better emulates the actual environmental impact results of the training data products.

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

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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.

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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

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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