Chen, Xinzhong, Ren, Richard, and Liu, Mengkun. Validity of Machine Learning in the Quantitative Analysis of Complex Scanning Near-Field Optical Microscopy Signals Using Simulated Data. Retrieved from https://par.nsf.gov/biblio/10237593. Physical Review Applied 15.1 Web. doi:10.1103/PhysRevApplied.15.014001.

Chen, Xinzhong, Ren, Richard, & Liu, Mengkun. Validity of Machine Learning in the Quantitative Analysis of Complex Scanning Near-Field Optical Microscopy Signals Using Simulated Data. Physical Review Applied, 15 (1). Retrieved from https://par.nsf.gov/biblio/10237593. https://doi.org/10.1103/PhysRevApplied.15.014001

Chen, Xinzhong, Ren, Richard, and Liu, Mengkun. "Validity of Machine Learning in the Quantitative Analysis of Complex Scanning Near-Field Optical Microscopy Signals Using Simulated Data". Physical Review Applied 15 (1). Country unknown/Code not available. https://doi.org/10.1103/PhysRevApplied.15.014001. https://par.nsf.gov/biblio/10237593.

@article{osti_10237593,
place = {Country unknown/Code not available}, title = {Validity of Machine Learning in the Quantitative Analysis of Complex Scanning Near-Field Optical Microscopy Signals Using Simulated Data}, url = {https://par.nsf.gov/biblio/10237593}, DOI = {10.1103/PhysRevApplied.15.014001}, abstractNote = {}, journal = {Physical Review Applied}, volume = {15}, number = {1}, author = {Chen, Xinzhong and Ren, Richard and Liu, Mengkun}, editor = {null} }