Web. Learn more about Lindsay Pinnick's work experience, education, connections & more by visiting their profile on LinkedIn WebJul 16, 2024 · Here, we employ the emerging paradigm of physics-informed neural networks (PINNs) to solve the eikonal equation. By minimizing a loss function formed by imposing the validity of the eikonal equation, we train a neural network to produce traveltimes that are consistent with the underlying partial differential equation.
PINNeik: Eikonal solution using physics-informed neural networks ...
WebPINNeik: Eikonal solution using physics-informed neural networks Author: Umair bin Waheed, Ehsan Haghighat, Tariq Alkhalifah, Chao Song, Qi Hao Source: Computers & … WebPINNeik: Eikonal solution using physics-informed neural networks . Submitted. Preprint PDF Code Source Document Ehsan Haghighat, Ali Can Bekar, Erdogan Madenci, Ruben Juanes (2024). A nonlocal physics-informed deep learning framework using the peridynamic differential operator . Submitted. Preprint PDF Code E. Haghighat and R. Juanes (2024). pitch ambev
High-frequency wavefield extrapolation using the Fourier neural ...
WebPINNeik: Eikonal solution using physics-informed neural networks. Umair bin Waheed, Ehsan Haghighat, Tariq Ali Alkhalifah, Chao Song, Qi Hao. Earth Science and … WebFeb 1, 2024 · Abstract. The concept of physics-informed neural networks has become a useful tool for solving differential equations due to its flexibility. A few approaches use this concept to solve the eikonal equation that describes the first-arrival traveltimes of waves propagating in smooth heterogeneous velocity models. WebPINNeik: Eikonal solution using physics-informed neural networks: dc.type: Article: dc.contributor.department: Earth Science and Engineering Program: … pitch altitude