Latent Force Model

Latent force models aim to represent complex dynamical systems by inferring underlying, often unobservable, forces that govern their behavior. Current research focuses on developing sophisticated model architectures, such as those incorporating deep Gaussian processes, neural fields, and equivariant transformers, to accurately capture these forces from observed system dynamics, even in highly nonlinear scenarios. These advancements are improving the accuracy and efficiency of simulations across diverse fields, from polymer physics and molecular dynamics to robotics and autonomous vehicle control, by providing more robust and generalizable force field representations. The resulting improved predictive capabilities have significant implications for scientific modeling and engineering applications.