In recent years, nonlinear dynamic system identification using artificial neural networks has garnered attention due to its broad potential applications across science and engineering. However, purely data-driven approaches often struggle with extrapolation and may yield physically implausible forecasts. Furthermore, the learned dynamics can exhibit instabilities, making it difficult to apply such models safely and robustly. This article introduces stable port-Hamiltonian neural networks, a machine learning architecture that incorporates physical biases of energy conservation and dissipation while ensuring global Lyapunov stability of the learned dynamics. Through illustrative and real-world examples, we demonstrate that these strong inductive biases facilitate robust learning of stable dynamics from sparse data, while avoiding instability and surpassing purely data-driven approaches in accuracy and physically meaningful generalization. Furthermore, the model's applicability and potential for data-driven surrogate modeling are showcased on multi-physics simulation data.

In this work, we introduce KnitKit , a flexible and customizable system for the computational design and production of functional, multi-material, and three-dimensional knitted textiles. Our system greatly simplifies the knitting of 3D objects with complex, varying patterns that use multiple yarns and stitch patterns by separating the high-level design specification in terms of geometry, stitch patterns, materials or colors from the low-level, machine-specific knitting instruction generation. Starting from a triangular 3D mesh and a 2D texture that specifies knitting patterns on top of the geometry, our system generates the required machine instructions in three major steps. First, the input is processed and the KnitNet data structure is generated. This graph structure serves as an abstract interface between the high-level geometric and knitting configuration and the low-level, machine-specific knitting instructions. Second, a graph rewriting procedure is applied on the KnitNet that produces a sequence of abstract machine actions. Finally, the low-level machine instructions are generated by adapting those abstract actions to a specific machine context. We showcase the potential of this computational approach by designing and fabricating a variety of objects with complex geometries, multiple yarns, and multiple stitch patterns.