Soft robots' flexibility and compliance give them the potential to outperform traditional rigid-
bodied robots while performing multiple tasks in unexpectedly changing environments and …

Accurate hydrological understanding and water cycle prediction are crucial for addressing
scientific and societal challenges associated with the management of water resources …

We introduce Neural Flow Maps, a novel simulation method bridging the emerging
paradigm of implicit neural representations with fluid simulation based on the theory of flow …

Brain-body co-design, which involves the collaborative design of control strategies and
morphologies, has emerged as a promising approach to enhance a robot's adaptability to its …

Differentiable physics simulation has shown its efficacy in inverse design problems. Given
the pervasiveness of the diverse interactions between fluids and solids in life, a …

While significant research progress has been made in robot learning for control, unique
challenges arise when simultaneously co-optimizing morphology. Existing work has typically …

Differentiable simulators continue to push the state of the art across a range of domains
including computational physics, robotics, and machine learning. Their main value is the …

We present Aquarium, a differentiable fluid-structure interaction solver for robotics that offers
stable simulation, accurately coupled fluid-robot physics in two dimensions, and full …

Numerical simulation of physical systems has become an increasingly important scientific
tool supporting various research fields. Despite its remarkable success, simulating intricate …

Design optimization of soft actuators is essential for task-oriented applications. Models
derived from analytical solutions, the Finite Element Method (FEM), or empirical …