no code implementations • 24 Jan 2024 • Vasiliy A. Es'kin, Danil V. Davydov, Julia V. Gur'eva, Alexey O. Malkhanov, Mikhail E. Smorkalov
A method for solving elasticity problems based on separable physics-informed neural networks (SPINN) in conjunction with the deep energy method (DEM) is presented.
no code implementations • 5 Apr 2023 • Vasiliy A. Es'kin, Danil V. Davydov, Ekaterina D. Egorova, Alexey O. Malkhanov, Mikhail A. Akhukov, Mikhail E. Smorkalov
The advantage of using the modified problem for physics-informed neural networks (PINNs) methodology is that it becomes possible to represent the loss function in the form of a single term associated with differential equations, thus eliminating the need to tune the scaling coefficients for the terms related to boundary and initial conditions.
no code implementations • 24 Jan 2018 • Srinivas Sridharan, Karthikeyan Vaidyanathan, Dhiraj Kalamkar, Dipankar Das, Mikhail E. Smorkalov, Mikhail Shiryaev, Dheevatsa Mudigere, Naveen Mellempudi, Sasikanth Avancha, Bharat Kaul, Pradeep Dubey
The exponential growth in use of large deep neural networks has accelerated the need for training these deep neural networks in hours or even minutes.