Infinite Domain

Research on infinite domains focuses on developing methods for handling problems where one or more variables are unbounded, a challenge arising in diverse fields like machine learning and physics. Current efforts concentrate on adapting existing algorithms, such as online learning and physics-informed neural networks (PINNs), to these unbounded spaces, often incorporating techniques like adversarial training or spectral methods to improve accuracy and efficiency. These advancements are crucial for tackling complex real-world problems, including seismic wave modeling and robust machine learning under distributional shifts, where traditional bounded-domain assumptions fail.