no code implementations • 25 Oct 2022 • Rini J. Gladstone, Mohammad A. Nabian, N. Sukumar, Ankit Srivastava, Hadi Meidani
Physics-Informed Neural Networks (PINNs) are a class of deep learning neural networks that learn the response of a physical system without any simulation data, and only by incorporating the governing partial differential equations (PDEs) in their loss function.
no code implementations • 17 Apr 2021 • N. Sukumar, Ankit Srivastava
In this paper, we introduce a new approach based on distance fields to exactly impose boundary conditions in physics-informed deep neural networks.
no code implementations • 1 Nov 2020 • Eric B. Chin, N. Sukumar
This paper introduces the scaled boundary cubature (SBC) scheme for accurate and efficient integration of functions over polygons and two-dimensional regions bounded by parametric curves.
Numerical Analysis Numerical Analysis
