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Machine learning-based methodology for assessment of Doppler reactivity of sodium-cooled fast reactor
Petrović, Đ., & Mikityuk, K. (2021). Machine learning-based methodology for assessment of Doppler reactivity of sodium-cooled fast reactor. Journal of Nuclear Engineering and Radiation Science, 7(4), 042004 (6 pp.). https://doi.org/10.1115/1.4050216
Neural network-based prediction for surface characteristics in CO<sub>2</sub> laser micro-milling of glass fiber reinforced plastic composite
Prakash, S., & Suman, S. (2021). Neural network-based prediction for surface characteristics in CO2 laser micro-milling of glass fiber reinforced plastic composite. Neural Computing & Applications (13 pp.). https://doi.org/10.1007/s00521-021-05818-w
Visualizing and analyzing 3D metal nanowire networks for stretchable electronics
Forró, C., Ihle, S. J., Reichmuth, A. R., Han, H., Stauffer, F., Weaver, S., … Vörös, J. (2020). Visualizing and analyzing 3D metal nanowire networks for stretchable electronics. Advanced Theory and Simulations, 3(8), 2000038 (10 pp.). https://doi.org/10.1002/adts.202000038
PyFitit: the software for quantitative analysis of XANES spectra using machine-learning algorithms
Martini, A., Guda, S. A., Guda, A. A., Smolentsev, G., Algasov, A., Usoltsev, O., … Soldatov, A. V. (2020). PyFitit: the software for quantitative analysis of XANES spectra using machine-learning algorithms. Computer Physics Communications, 250, 107064 (15 pp.). https://doi.org/10.1016/j.cpc.2019.107064
Neural network based process coupling and parameter upscaling in reactive transport simulations
Prasianakis, N. I., Haller, R., Mahrous, M., Poonoosamy, J., Pfingsten, W., & Churakov, S. V. (2020). Neural network based process coupling and parameter upscaling in reactive transport simulations. Geochimica et Cosmochimica Acta, 291, 126-143. https://doi.org/10.1016/j.gca.2020.07.019
Imaging nanoscale lattice variations by machine learning of x-ray diffraction microscopy data
Laanait, N., Zhang, Z., & Schlepütz, C. M. (2016). Imaging nanoscale lattice variations by machine learning of x-ray diffraction microscopy data. Nanotechnology, 27(37), 374002 (10 pp.). https://doi.org/10.1088/0957-4484/27/37/374002