Deep learning at 15PF: supervised and semi-supervised classification for scientific data. - Thorsten Kurth; Jian Zhang 049; Nadathur Satish; Evan Racah; Ioannis Mitliagkas; Md. Mostofa Ali Patwary; Tareq M. Malas; Narayanan Sundaram; Wahid Bhimji; Mikhail Smorkalov; Jack Deslippe; Mikhail Shiryaev; et al. (SC, 2017)

On Scale-out Deep Learning Training for Cloud and HPC. - Srinivas Sridharan 002; Karthikeyan Vaidyanathan; Dhiraj D. Kalamkar; Dipankar Das 002; Mikhail E. Smorkalov; Mikhail Shiryaev; Dheevatsa Mudigere; Naveen Mellempudi; Sasikanth Avancha; Bharat Kaul; Pradeep Dubey (CoRR, 2018)

Training Google Neural Machine Translation on an Intel CPU Cluster. - Dhiraj D. Kalamkar; Kunal Banerjee 001; Sudarshan Srinivasan; Srinivas Sridharan 002; Evangelos Georganas; Mikhail E. Smorkalov; Cong Xu; Alexander Heinecke (CLUSTER, 2019)

TensorFlow at Scale: Performance and productivity analysis of distributed training with Horovod, MLSL, and Cray PE ML. - Thorsten Kurth; Mikhail Smorkalov; Peter Mendygral; Srinivas Sridharan 002; Amrita Mathuriya (Concurr. Comput. Pract. Exp., 2019)

About optimal loss function for training physics-informed neural networks under respecting causality. - Vasiliy A. Es'kin; Danil V. Davydov; Ekaterina D. Egorova; Alexey O. Malkhanov; Mikhail A. Akhukov; Mikhail E. Smorkalov (CoRR, 2023)

Catenary and Mercator projection. - Mikhail A. Akhukov; Vasiliy A. Es'kin; Mikhail E. Smorkalov (CoRR, 2024)

Separable Physics-Informed Neural Networks for the solution of elasticity problems. - Vasiliy A. Es'kin; Danil V. Davydov; Julia V. Gur'eva; Alexey O. Malkhanov; Mikhail E. Smorkalov (CoRR, 2024)

About rectified sigmoid function for enhancing the accuracy of Physics-Informed Neural Networks. - Vasiliy A. Es'kin; Alexey O. Malkhanov; Mikhail E. Smorkalov (CoRR, 2024)

Are two hidden layers still enough for the physics-informed neural networks? - Vasiliy A. Es'kin; Alexey O. Malkhanov; Mikhail E. Smorkalov (J. Comput. Phys., 2025)