Two Level Lattice Neural Network

Two-level lattice neural networks (TLLNNs) are a specialized neural network architecture leveraging a structured, lattice-based organization to improve efficiency and interpretability in various applications. Current research focuses on developing and applying TLLNNs for tasks such as material property prediction, modeling dynamical systems (including working memory), and efficient surrogate modeling of complex simulations, often incorporating techniques like graph neural operators and vector quantization. The ability of TLLNNs to offer both speed and accuracy improvements, particularly in computationally expensive simulations and analyses, makes them a significant area of investigation with potential impact across diverse scientific and engineering domains.