洛克希德·马丁

Liquid crystal elastomers (LCEs) are polymers with nematic ordering that exhibit unique mechanical behaviors attributed to the interplay of network stretching and mesogen reorientation under loading. These competing viscoelastic mechanisms can lead to complex and heterogeneous local deformation response when exposed to an external stimulus such as light, thermal gradients, or applied stress. This study developed stereo digital image correlation (stereo-DIC) measurements and analyses of strain fields in polydomain LCE specimens undergoing the polydomain-monodomain (P-M) transition to reveal deformation mechanisms. Two macro-scale deformation modes probed the material response: quasi-static uniaxial extension and stepped stress-relaxation. Stereo-DIC tracked the full-field surface strain. A bespoke technique was developed consisting of a clustering algorithm to identify coherent strain clusters in the strain field at the maximum stress of the step loading and a stretched exponential model fit to the time-evolving strain response of the clusters and fitting to the stress relaxation response. This technique quantified differences in the viscoelasticity of the domains involved in the P-M transition. Strain maps during the hold period showed that strain heterogeneity increased with time. Strain in the majority of clusters either increased asymptotically or decreased asymptotically with time, leading to increasing strain heterogeneity. The characteristic relaxation times varied between the clusters and were different from that of the macroscopic stress response. A new technique for quantifying surface strain clustering is available. Initial findings on LCEs suggest that the kinematics of viscous mesogen rotation depends on the local strain state.

This paper presents a block-structured formulation of Operator Inference as a way to learn structured reduced-order models for multiphysics systems. The approach specifies the governing equation structure for each physics component and the structure of the coupling terms. Once the multiphysics structure is specified, the reduced-order model is learned from snapshot data following the nonintrusive Operator Inference methodology. In addition to preserving physical system structure, which in turn permits preservation of system properties such as stability and second-order structure, the block-structured approach has the advantages of reducing the overall dimensionality of the learning problem and admitting tailored regularization for each physics component. The numerical advantages of the block-structured formulation over a monolithic Operator Inference formulation are demonstrated for aeroelastic analysis, which couples aerodynamic and structural models. For the benchmark test case of the AGARD 445.6 wing, block-structured Operator Inference provides an average 20% online prediction speedup over monolithic Operator Inference across subsonic and supersonic flow conditions in both the stable and fluttering parameter regimes while preserving the accuracy achieved with monolithic Operator Inference.

Robots solving generalist tasks need to be able to ground instructions in their past experience, since humans may refer to notable past events when giving a task (e.g., “Take me to where the chemical spill happened yesterday”). Since memory limits make storing all past events infeasible, long-term robot memory must be selective, ideally retaining only those episodes with high utility for future tasks. However, future tasks are not typically given a priori for generalist robots. To select generically useful memories, we propose Bayesian surprise as a gating mechanism for memory formation. We present an approach to compute surprise in a semantically rich deployment-agnostic latent space provided by V-JEPA-2. Using our gated episodic memory to augment 4D scene graph-based spatial memory, we show a consistent improvement over state-of-the-art benchmarks in robot question answering, outperforming prior robot memory methods by ≥12% for temporal, spatial, and binary questions, and surpassing the performance of supervised and non-causal methods with an unsupervised causal method in event segmentation tasks.

In this paper, we develop a stratification-based semantics for Signal Temporal Logic (STL) in which each atomic predicate is interpreted as a membership test in a stratified space. This perspective reveals a novel correspondence principle between stratification theory and STL, showing that most STL formulas can be viewed as inducing a stratification of space-time. The significance of this interpretation is twofold. First, it offers a fresh theoretical framework for analyzing the structure of the embedding space generated by deep reinforcement learning (DRL) and relates it to the geometry of the ambient decision space. Second, it provides a principled framework that both enables the reuse of existing high-dimensional analysis tools and motivates the creation of novel computational techniques. To ground the theory, we (1) illustrate the role of stratification theory in Minigrid games and (2) apply numerical techniques to the latent embeddings of a DRL agent playing such a game where the robustness of STL formulas is used as the reward. In the process, we propose computationally efficient signatures that, based on preliminary evidence, appear promising for uncovering the stratification structure of such embedding spaces.

The work function is a key surface property that plays a prominent role in electronic transport and the thermoelectric (TE) properties of a TE material. For TE nanocomposite materials, the jump in the work function at the interface of the constituent materials can impact the figure of merit (ZT) of the system. The work function in turn is sensitive to the interfacial surface structure. In this work, the effects of the surface structure and atomic termination on the surface dipole and work function of various La3Te4 slab structures were investigated using first-principles electronic structure calculations. The computed surface dipole and work function of La3Te4 slabs are found to depend not only on the atomic surface structure, growth direction, and termination, but also on the composition of each surface component (Te-rich or La-rich). We also discovered that while the surface electron density can explain the trends in the work function with modifications of La-rich surfaces, it fails to do so in the case of Te-rich surfaces. On the other hand, changes in the work function can be explained by the electronic dipole density at the surface. We also propose a new way to estimate the surface dipole using first principles calculations, and the relationship between the work function and ZT of the two nanocomposite systems La3Te4-Ni and La3Te4-Ca is discussed.