Where

  T  is (Traceability Matrix ) a Tensor. Tensor is a multidimensional array with a uniform data type as dtype. Update a tensor is not defined   but create a new one. \

 If  TensorFlow library is used for tensor then TensorFlow decide when to use GPU or when to use CPU as the TensorFlow library automatically takes care of that. TensorFlow support CPU/GPU/TPU and also automatic Differentiation.  Values can be numerical / Strings. do not try to append, insert, delete or change a value to a tensor. Tensors used in TensorFlow and also in PyTorch ( these two are Deep  Learning platforms )

  Z  is   a Tensor ( it can be  Scalar, Vector, Matrix  , Tensor etc)

 F is a function from Tensor T (Traceability Matrix ) to another Tensor Z.

Since Function F is defined on Tensor T, it is possible to compute Derivative of T.  ( GPU is very useful to compute Derivative of F)

Use of Tensor to represent Data is a good option. Above shows it is not good to use numpy.ndarray to represent Data.  A tensor is a more suitable choice if GPU is available for computing.   tensor  can reside in  GPU accelerators memory. Tensors are immutable

How do  make it meaningful in connecting Digital Thread  with what users do day-to-day? 

Do we combine technical expertise with business knowledge and Digital Thread Key topics include life cycles, development approaches, requirements and how to make a business case? 

How do we measure improved Digital Thread service perception, and satisfaction? 

More effective Digital Thread leader? 

Can Management personnel recognize the monetary benefit of Digital Thread? 

Building a Digital Thread Architecture 

Building a Digital Thread 

Building Queries ( graph, voice )

Building AI inference ( Data Analytics )

Building Digital Pipeline