Alemazkoor, Negin, Louhghalam, Arghavan, and Tootkaboni, Mazdak. A Multi-fidelity Polynomial Chaos-Greedy Kaczmarz Approach for Resource-Efficient Uncertainty Quantification on Limited Budget. Retrieved from https://par.nsf.gov/biblio/10318579. Computer methods in applied mechanics and engineering 389.1 Web. doi:10.1016/j.cma.2021.114290.

Alemazkoor, Negin, Louhghalam, Arghavan, & Tootkaboni, Mazdak. A Multi-fidelity Polynomial Chaos-Greedy Kaczmarz Approach for Resource-Efficient Uncertainty Quantification on Limited Budget. Computer methods in applied mechanics and engineering, 389 (1). Retrieved from https://par.nsf.gov/biblio/10318579. https://doi.org/10.1016/j.cma.2021.114290

Alemazkoor, Negin, Louhghalam, Arghavan, and Tootkaboni, Mazdak. "A Multi-fidelity Polynomial Chaos-Greedy Kaczmarz Approach for Resource-Efficient Uncertainty Quantification on Limited Budget". Computer methods in applied mechanics and engineering 389 (1). Country unknown/Code not available. https://doi.org/10.1016/j.cma.2021.114290. https://par.nsf.gov/biblio/10318579.

@article{osti_10318579,
place = {Country unknown/Code not available}, title = {A Multi-fidelity Polynomial Chaos-Greedy Kaczmarz Approach for Resource-Efficient Uncertainty Quantification on Limited Budget}, url = {https://par.nsf.gov/biblio/10318579}, DOI = {10.1016/j.cma.2021.114290}, abstractNote = {}, journal = {Computer methods in applied mechanics and engineering}, volume = {389}, number = {1}, author = {Alemazkoor, Negin and Louhghalam, Arghavan and Tootkaboni, Mazdak}, }