activity
20162023
most citedEfficient training of physics-informed neural networks via importance sampling

326 citations · 1k across the 19 of their papers we have counts for

collaborators
Showing 2016Show all

1 paper · 1 filter

stat.CO2016★ 24 cited

Divide and Conquer: An Incremental Sparsity Promoting Compressive Sampling Approach for Polynomial Chaos Expansions

Negin Alemazkoor, Hadi Meidani

This paper introduces an efficient sparse recovery approach for Polynomial Chaos (PC) expansions, which promotes the sparsity by breaking the dimensionality of the problem. The pro…