The COVID-19 pandemic emphasized the importance of epidemic forecasting for decision
makers in multiple domains, ranging from public health to the economy. Forecasting …

Integrating prior epidemiological knowledge embedded within mechanistic models with the
data-mining capabilities of artificial intelligence (AI) offers transformative potential for …

Our recent study has found that physics-informed neural networks (PINN) tend to be local
approximators after training. This observation led to the development of a novel physics …

Urban flow prediction is a spatio-temporal modelling task that estimates the throughput of
transportation services like buses, taxis, and ride-sharing, where data-driven models have …

Physics-Informed Neural Networks (PINNs), which incorporate PDEs as soft constraints,
train with a composite loss function that contains multiple training point types: different types …

Effective epidemic forecasting is critical for public health strategies and efficient medical
resource allocation, especially in the face of rapidly spreading infectious diseases. However …

Mechanistic simulators are an indispensable tool for epidemiology to explore the behavior of
complex, dynamic infections under varying conditions and navigate uncertain environments …

Measles is an important infectious disease system both for its burden on public health and
as an opportunity for studying nonlinear spatio-temporal disease dynamics. Traditional …

The prediction of the evolution of epidemics plays an important role in limiting the
transmissibility and the burdensome consequences of infectious diseases, which leads to …

The rapid spread of rumors on social media has posed significant challenges to maintaining
public trust and information integrity. Since an information cascade process is essentially a …