“…Typically, the high-dimensional fluid and structure dynamics are reduced into low-dimensional latent spaces using techniques like POD or convolutional auto-encoders, and the latent fluid and structure dynamics are learned by separate DNNs [23]. Physical interface constraints, such as moving interfaces (solid-to-fluid coupling) and fluid forces (fluid-to-solid coupling), are often used to couple the fluid and structure DNNs, which can be represented using methods like level-set functions [23][24][25], immersed boundary method (IBM) masks [26], or direct forcing terms [27]. By doing so, both the structural responses and the fluid dynamics can be learned in a consistent manner.…”