Deep Energy

Deep Energy methods leverage the principles of energy minimization within deep learning frameworks to solve complex physical problems, primarily focusing on improving the accuracy and efficiency of existing numerical techniques. Current research emphasizes the development of geometry-aware models and the application of various neural network architectures, such as Physics-Informed Neural Networks (PINNs) and separable PINNs, often within the context of the Deep Energy Method (DEM) or its complementary energy counterpart. This approach shows promise for applications in diverse fields, including structural mechanics, image reconstruction, and building energy optimization, by offering faster and more accurate solutions compared to traditional methods.