This study introduces a hybrid machine learning-based scale-bridging framework for predicting the permeability of fibrous textile structures. By addressing the computational challenges inherent to multiscale modeling, the proposed approach evaluates the efficiency and accuracy of different scale-bridging methodologies combining traditional surrogate models and even integrating physics-informed neural networks (PINNs) with numerical solvers, enabling accurate permeability predictions across micro- and mesoscales. Four methodologies were evaluated: Single Scale Method (SSM), Simple Upscaling Method (SUM), Scale-Bridging Method (SBM), and Fully Resolved Model (FRM). SSM, the simplest method, neglects microscale permeability and exhibited permeability values deviating by up to 150\% of the FRM model, which was taken as ground truth at an equivalent lower fiber volume content. SUM improved predictions by considering uniform microscale permeability, yielding closer values under similar conditions, but still lacked structural variability. The SBM method, incorporating segment-based microscale permeability assignments, showed significant enhancements, achieving almost equivalent values while maintaining computational efficiency and modeling runtimes of ~45 minutes per simulation. In contrast, FRM, which provides the highest fidelity by fully resolving microscale and mesoscale geometries, required up to 270 times more computational time than SSM, with model files exceeding 300 GB. Additionally, a hybrid dual-scale solver incorporating PINNs has been developed and shows the potential to overcome generalization errors and the problem of data scarcity of the data-driven surrogate approaches. The hybrid framework advances permeability modelling by balancing computational cost and prediction reliability, laying the foundation for further applications in fibrous composite manufacturing.

We introduce the level-ordered edge sequence (LOES), a suc- cinct encoding for state-sets based on prefix-trees. For use in state-space search, we give algorithms for member testing and element hashing with runtime dependent only on state- size, as well as space and memory efficient construction of and iteration over such sets. Finally we compare LOES to binary decision diagrams (BDDs) and explicitly packed set- representation over a range of IPC planning problems. Our results show LOES produces succinct set-encodings for a wider range of planning problems than both BDDs and ex- plicit state representation, increasing the number of problems that can be solved cost-optimally.

On interactive surfaces, an accurate system calibration is crucial for a precise user interaction. Today, geometric distortions are eliminated by a static calibration. However, this calibration is specific to a user’s posture, and parallax distortions occur if this changes (i.e. if the user moves or if multiple users take turns). Within this paper, we describe an approach to model automatic online re-calibration to cope with changing viewpoints by using Partially Observable Markov Decision Processes (POMDP). Hereby, the viewpoint is stochastically deducted from the precision of user interactions on the surface. To enable the implementation on embedded systems, a small model is defined using states and observations, which are formulated relative to the current assumed viewpoint. We show the structure of a family of models, that can be generated automatically based on the user’s position probability and pointing accuracy.