146 citations · 163 across the 4 of their papers we have counts for
4 papers
Accelerating the discovery of steady-states of planetary interior dynamics with machine learning
Siddhant Agarwal, Nicola Tosi, Christian Hüttig +2
Simulating mantle convection often requires reaching a computationally expensive steady-state, crucial for deriving scaling laws for thermal and dynamical flow properties and bench…
Solving the Eikonal equation for compressional and shear waves in anisotropic media using peridynamic differential operator
Ali Can Bekar, Erdogan Madenci, Ehsan Haghighat +2
The traveltime of compressional (P) and shear (S) waves have proven essential in many applications of earthquake and exploration seismology. An accurate and efficient traveltime co…
Deep learning for solution and inversion of structural mechanics and vibrations
Ehsan Haghighat, Ali Can Bekar, Erdogan Madenci +1
Deep learning has been the most popular machine learning method in the last few years. In this chapter, we present the application of deep learning and physics-informed neural netw…
A nonlocal physics-informed deep learning framework using the peridynamic differential operator
Ehsan Haghighat, Ali Can Bekar, Erdogan Madenci +1
The Physics-Informed Neural Network (PINN) framework introduced recently incorporates physics into deep learning, and offers a promising avenue for the solution of partial differen…