Informed Neural Network

Informed neural networks integrate physical principles into neural network architectures to solve complex scientific problems, primarily focusing on efficiently and accurately solving partial differential equations (PDEs) and related inverse problems. Current research emphasizes improving the accuracy and speed of these networks, exploring architectures like physically informed neural networks (PINNs) and bilinear neural networks (BNNMs), and incorporating techniques such as coarse space acceleration to enhance scalability for large-scale applications. This approach holds significant promise for accelerating scientific discovery and engineering design by providing faster and more accurate solutions to problems previously intractable with traditional numerical methods.