Tensorflow does general computation using data flow graphs; you assemble your graph from operations and variables (tensors, as they're called these days), and T

Tensorflow does general computation using data flow graphs; you assemble your graph from operations and variables (tensors, as they're called these days), and Tensorflow handles distribution of this computation over hardware which you make available. A quick google gives these [1] impressive results for Tensorflow, at least for linear algebra operations. Despite the advantages, I think you'll find many more readily available functions in Numpy for what you want, while Tensorflow remains quite 'low level', exposing building block operations rather than higher-level methods (the exception is machine learning/neural network stuff). That said, I don't imagine it would be too difficult to implement a fast quadrature method for integration, or whatever else your heart might desire. This [2] is a simple example solving a PDE. ----- [1] https://simplyml.com/linear-algebra-shootout-numpy-vs-theano-vs-tensorflow-2/ https://simplyml.com/linear-algebra-shootout-numpy-vs-theano... [2] https://www.tensorflow.org/tutorials/pdes/ https://www.tensorflow.org/tutorials/pdes/