Fuhg, Jan N., and Bouklas, Nikolaos. On physics-informed data-driven isotropic and anisotropic constitutive models through probabilistic machine learning and space-filling sampling. Retrieved from https://par.nsf.gov/biblio/10389931. Computer Methods in Applied Mechanics and Engineering 394.C Web. doi:10.1016/j.cma.2022.114915.

Fuhg, Jan N., & Bouklas, Nikolaos. On physics-informed data-driven isotropic and anisotropic constitutive models through probabilistic machine learning and space-filling sampling. Computer Methods in Applied Mechanics and Engineering, 394 (C). Retrieved from https://par.nsf.gov/biblio/10389931. https://doi.org/10.1016/j.cma.2022.114915

Fuhg, Jan N., and Bouklas, Nikolaos. "On physics-informed data-driven isotropic and anisotropic constitutive models through probabilistic machine learning and space-filling sampling". Computer Methods in Applied Mechanics and Engineering 394 (C). Country unknown/Code not available. https://doi.org/10.1016/j.cma.2022.114915. https://par.nsf.gov/biblio/10389931.

@article{osti_10389931,
place = {Country unknown/Code not available}, title = {On physics-informed data-driven isotropic and anisotropic constitutive models through probabilistic machine learning and space-filling sampling}, url = {https://par.nsf.gov/biblio/10389931}, DOI = {10.1016/j.cma.2022.114915}, abstractNote = {}, journal = {Computer Methods in Applied Mechanics and Engineering}, volume = {394}, number = {C}, author = {Fuhg, Jan N. and Bouklas, Nikolaos}, }