aLL-i @MOveOPattern_v01
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engineer-i design engineering suite of AI tool kit series : technology  

We  do data driven design  engineering using AI technologies, design and model featured  by API on ANN architecture to empower  every aspect of engineering practise.


Our passion

We are passionate about integrating machine learning/deep learning  algorithms and data analytics into engineering design,  as in suite of  design engineering tool kit, enabled to empower design engineers, to develop, create, update innovate  products.   engineer-i  suite of APIs ,  is  designed and enhanced on cloud/edge computing, processed, trained using   data generated and classified on  neural network  architecture in cluster by  power of neural and quantum processors.

 Our mission

We do design and develop algorithms, using  machine learning/Deep learning algorithms, to develop surrogate or hybrid  quantum models, to solve design models.  Surrogate models  developed  using artificial neural network in quantum domain, in order to estimate design performance, through life cycles of design, checked, validated and verified against captured design requirements. All are scripted on python.

    Our task 

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. Quantum computing  in this process, has  offered a viable path towards  efficient multi-parameter optimization covering the entire design space.  The challenge arises when computing a broad range of design configurations simultaneously which is currently not possible with classical computing. Structural integrity can be  demonstrated by simulating key occurrences through  life cycles of design,  required  by regulations and captured design requirements.

                                    Our technology

engineer-i  API’s enabled by methods  (i) development of quantum-hybrid approach code, based on development of solver using open-source free software to analyse and design propulsion plant, to assess performance through life cycles of design as classical systems extension to quantum states  A 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 PDE,  (ii) unconventional Artificial Neural Network surrogate model in order avoid the problem of heavy computational burdens that inherently exist in Monte Carlo simulation

engineer-i design engineering suite of AI tool kit series: Design Algorithms 

CPP-01 Quantum Hyrid Solver on ANN_v0210
CP_03 Learning Surrogate LCA_v021024_1.j
CP_02 Surrogate Model on ANN_v021024_1.j

engineer-i design  engineering suite of AI tool kit series: APIs 

contact at  all-i@MOveOaLL-i@MOveO by moveo-i@MOveO limited

  • monamie-i@MOveO

    Design engineer companion to AI and ML technologies and technologies behind.

  • purple-i@MOveO

    Online AI technologies retail store and Robot bargain shop, to serve as unique one stop shop.

  • engineer-i@MOveO

    engineering design intelligence, featured API s operating on web processed in cloud.

  • all-i@MOveO

    serve to empower innovative minds, enabled by breakthrough technologies in cloud cluster.

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