A blueprint for building efficient Neural Network Differential Equation Solvers

9 Jul 2020Akshunna S. Dogra

Neural Networks are well known to have universal approximation properties for wide classes of Lebesgue integrable functions. We describe a collection of strategies and applications sourced from various fields of mathematics and physics to detail a rough blueprint for building efficient Neural Network differential equation solvers...

PDF Abstract


No code implementations yet. Submit your code now


Results from the Paper

  Submit results from this paper to get state-of-the-art GitHub badges and help the community compare results to other papers.

Methods used in the Paper

🤖 No Methods Found Help the community by adding them if they're not listed; e.g. Deep Residual Learning for Image Recognition uses ResNet