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