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Abstract Molecular dynamics (MD) has served as a powerful tool for designing materials with reduced reliance on laboratory testing. However, the use of MD directly to treat the deformation and failure of materials at the mesoscale is still largely beyond reach. In this work, we propose a learning framework to extract a peridynamics model as a mesoscale continuum surrogate from MD simulated material fracture data sets. Firstly, we develop a novel coarse-graining method, to automatically handle the material fracture and its corresponding discontinuities in the MD displacement data sets. Inspired by the weighted essentially non-oscillatory (WENO) scheme, the key idea lies at an adaptive procedure to automatically choose the locally smoothest stencil, then reconstruct the coarse-grained material displacement field as the piecewise smooth solutions containing discontinuities. Then, based on the coarse-grained MD data, a two-phase optimization-based learning approach is proposed to infer the optimal peridynamics model with damage criterion. In the first phase, we identify the optimal nonlocal kernel function from the data sets without material damage to capture the material stiffness properties. Then, in the second phase, the material damage criterion is learnt as a smoothed step function from the data with fractures. As a result, a peridynamics surrogate is obtained. As a continuum model, our peridynamics surrogate model can be employed in further prediction tasks with different grid resolutions from training, and hence allows for substantial reductions in computational cost compared with MD. We illustrate the efficacy of the proposed approach with several numerical tests for the dynamic crack propagation problem in a single-layer graphene. Our tests show that the proposed data-driven model is robust and generalizable, in the sense that it is capable of modeling the initialization and growth of fractures under discretization and loading settings that are different from the ones used during training. 

Abstract A new measurement protocol, labeled Acoustic Mapping Velocimetry (AMV), has been successfully tested for in‐situ estimation of bedload transport features in sandy beds. The AMV has proven efficient in using the dune‐tracking method (DTM) for characterizing the bedform geometry and dynamics as well as for estimation of the rates of bedload transport. Given the novelty of the AMV protocol and its extensive reliance on multiple site‐specific assumptions and user‐defined parameters, a comparison of this emerging technique with other three non‐intrusive DTM‐based methods and analytical predictors is attempted in this paper. The comparison highlights that the AMV estimates are within 22% of the estimates with the other non‐intrusive protocols and up to 98% different from analytical predictions. The observed differences are related to the possible sources of uncertainty in the AMV workflows and to the means to reduce their impact on the targeted estimations. 

Pervasive behavioral and neural evidence for predictive processing has led to claims that language processing depends upon predictive coding. In some cases, this may reflect a conflation of terms, but predictive coding formally is a computational mechanism where only deviations from top- down expectations are passed between levels of representation. We evaluate three models’ ability to simulate predictive processing and ask whether they exhibit the putative hallmark of formal predictive coding (reduced signal when input matches expectations). Of crucial interest, TRACE, an interactive activation model that does not explicitly implement prediction, exhibits both predictive processing and model- internal signal reduction. This may indicate that interactive activation is functionally equivalent or approximant to predictive coding, or that caution is warranted in interpreting neural signal reduction as diagnostic of predictive coding. 

The Time-Invariant String Kernel (TISK) model of spoken word recognition (Hanngan et al., 2013) is an interactive activation model like TRACE (McClelland & Elman, 1986). However, it uses orders of magnitude fewer nodes and connections because it replaces TRACE's time-specific duplicates of phoneme and word nodes with time-invariant nodes based on a string kernel representation (essentially a phoneme-by-phoneme matrix, where a word is encoded as by all ordered open diphones it contains; e.g., cat has /kæ/, /æt/, and /kt/). Hannagan et al. (2013) showed that TISK behaves similarly to TRACE in the time course of phonological competition and even word-specific recognition times. However, the original implementation did not include feedback from words to diphone nodes, precluding simulation of top-down effects. Here, we demonstrate that TISK can be easily adapted to lexical feedback, affording simulation of top-down effects as well as allowing the model to demonstrate graceful degradation given noisy inputs. 

Despite the lack of invariance problem (the many-to-many mapping between acoustics and percepts), we experience phonetic constancy and typically perceive what a speaker intends. Models of human speech recognition have side- stepped this problem, working with abstract, idealized inputs and deferring the challenge of working with real speech. In contrast, automatic speech recognition powered by deep learning networks have allowed robust, real-world speech recognition. However, the complexities of deep learning architectures and training regimens make it difficult to use them to provide direct insights into mechanisms that may support human speech recognition. We developed a simple network that borrows one element from automatic speech recognition (long short-term memory nodes, which provide dynamic memory for short and long spans). This allows the network to learn to map real speech from multiple talkers to semantic targets with high accuracy. Internal representations emerge that resemble phonetically-organized responses in human superior temporal gyrus, suggesting that the model develops a distributed phonological code despite no explicit training on phonetic or phonemic targets. The ability to work with real speech is a major advance for cognitive models of human speech recognition.