Physics-Informed Neural Networks (PINNs) allow incorporating differential equations to model a system's behavior, and are well able to fit easily differentiable solutions with low-frequency components. They, however, fall short when attempting to learn high-frequency regions or boundary layers. This paper addresses one approach to fixing this by the addition of a Fourier Layer Neural Operator (FNO) to improve accuracy by better fitting regions with high frequency oscillations. We use as an example an underdamped RLC circuit, which resembles that of a boundary layer. The PINN architecture is built to be generalized over other applications, making it a flexible design for further expansion, especially for multiple layers of GPUs.

Recent studies have shown Graph Neural Networks (GNNs) are extremely vulnerable to attribute inference attacks. To tackle this challenge, existing privacy-preserving GNNs research assumes that the sensitive attributes of all users are known beforehand. However, due to different privacy preferences, some users (i.e., private users) may prefer not to reveal sensitive information that others (i.e., non-private users) would not mind disclosing. For example, in social networks, male users are typically less sensitive to their age information than female users. The age disclosure of male users can lead to the age information of female users in the network exposed. This is partly because social media users are connected, the homophily property and message-passing mechanism of GNNs can exacerbate individual privacy leakage. In this work, we study a novel and practical problem of learning privacy-preserving GNNs with partially observed sensitive attributes.