Mikhail Smorkalov

According to our database, Mikhail Smorkalov authored at least 12 papers between 2017 and 2026.

Collaborative distances:

• Dijkstra number of four.
• Erdős number of three.

2026

Dynamical Systems Theory Behind a Hierarchical Reasoning Model.

Vasiliy A. Es'kin, Mikhail E. Smorkalov

CoRR, March, 2026

Accuracy Recovery for Low-Precision PINNs: An Iterative Refinement Framework.

Mikhail E. Smorkalov, Bari R. Khairullin, Sergey G. Rykovanov

SN Comput. Sci., January, 2026

Learning the nonlinear plasma-mirror response: Fast surrogate modeling and optimization of ellipticity of attosecond pulses.

Mikhail Smorkalov, Rishat R. Zagidullin, C. Pan, J. Wang, Sergey G. Rykovanov

Commun. Nonlinear Sci. Numer. Simul., 2026

2025

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

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

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

Catenary and Mercator projection.

Mikhail A. Akhukov, Vasiliy A. Es'kin, Mikhail E. Smorkalov

CoRR, 2024

2023

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

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, Amrita Mathuriya

Concurr. Comput. Pract. Exp., 2019

Training Google Neural Machine Translation on an Intel CPU Cluster.

Dhiraj D. Kalamkar, Kunal Banerjee, Sudarshan Srinivasan, Srinivas Sridharan, Evangelos Georganas, Mikhail E. Smorkalov, Cong Xu, Alexander Heinecke

Proceedings of the 2019 IEEE International Conference on Cluster Computing, 2019

2018

On Scale-out Deep Learning Training for Cloud and HPC.

Srinivas Sridharan, Karthikeyan Vaidyanathan, Dhiraj D. Kalamkar, Dipankar Das, Mikhail E. Smorkalov, Mikhail Shiryaev, Dheevatsa Mudigere, Naveen Mellempudi, Sasikanth Avancha, Bharat Kaul, Pradeep Dubey

CoRR, 2018

2017

Deep learning at 15PF: supervised and semi-supervised classification for scientific data.

Thorsten Kurth, Jian Zhang, Nadathur Satish, Evan Racah, Ioannis Mitliagkas, Md. Mostofa Ali Patwary, Tareq M. Malas, Narayanan Sundaram, Wahid Bhimji, Mikhail Smorkalov, Jack Deslippe, Mikhail Shiryaev, Srinivas Sridharan, Prabhat, Pradeep Dubey

Proceedings of the International Conference for High Performance Computing, 2017