Inseung Kang

The paper introduces ANNet, a physics-informed neural network that unifies inverse and forward dynamics computations using a shared learned representation based on Appell's acceleration energy.

The paper shows that suboptimal locomotor performance in novel environments results from prioritizing fall risk avoidance, using an inverse adaptation model to infer internal learning parameters.