TensorValues.jl

++REPO NOT MAINTAINED++ Tensor values that behave like numbers in broadcasted operations
Author gridap
Popularity
2 Stars
Updated Last
2 Years Ago
Started In
May 2019

TensorValues

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TensorValues provides the types VectorValue (a 1-st order tensor), TensorValue (a 2-nd order tensor) and MultiValue (a generalization of VectorValue and TensorValue) and common tensor operations defined on these types (e.g., dot product, inner product, outer product, etc.)

Why

The main feature of the TensorValues package is that the provided types do not extend from AbstractArray, but from Number!

This allows one to work with them as if they were scalar values in broadcasted operations on arrays of VectorValue objects (also for TensorValue or MultiValue objects). For instance, one can perform the following manipulations:

# Assing a VectorValue to all the entries of an Array of VectorValues
A = zeros(VectorValue{2,Int}, (4,5))
v = VectorValue(12,31)
A .= v # This is posible since  VectorValue <: Number

# Broatcasing of tensor operations in arrays of TensorValues
t = TensorValue(13,41,53,17) # creates a 2x2 TensorValue
g = TensorValue(32,41,3,14) # creates another 2x2 TensorValue
B = fill(t,(1,5))
C = inner.(g,B) # inner product of g against all TensorValues in the array B
@show C
# C = [2494 2494 2494 2494 2494]

Installation

Pkg.add("TensorValues")

Required Packages

Used By Packages