SciANN: A TensorFlow API for scientific computations with neural networks | April 28, 2021

About the Webinar Over the past decade, artificial neural networks, also known as deep learning, have revolutionized many computational tasks, including image classification and computer vision, search engines and recommender systems, speech recognition, autonomous driving, and healthcare. Even more recently, this data-driven framework has made inroads in engineering and scientific applications, such as earthquake detection, fluid mechanics and turbulence modeling, dynamical systems, and constitutive modeling. A recent class of deep learning known as physics-informed neural networks (PINN), where the network is trained simultaneously on both data and the governing differential equations, is particularly well suited for solution and inversion of equations governing physical systems, in domains such as fluid mechanics, solid mechanics and dynamical systems. This increased interest in engineering and science is due to the increased availability of data and open-source platforms such as Theano, TensorFlow, and