scholarly journals Vulnerability analysis of large concrete dams using the continuum strong discontinuity approach and neural networks

2008 ◽  
Vol 30 (3) ◽  
pp. 217-235 ◽  
Author(s):  
Manolis Papadrakakis ◽  
Vissarion Papadopoulos ◽  
Nikos D. Lagaros ◽  
Javier Oliver ◽  
Alfredo E. Huespe ◽  
...  
Keyword(s):  
Neural Networks ◽  
Vulnerability Analysis ◽  
Strong Discontinuity ◽  
Concrete Dams ◽  
Continuum Strong Discontinuity Approach ◽  
The Continuum

scholarly journals Continuum Approach to Computational Multi-Scale Modeling of Fracture

2014 ◽  
Vol 627 ◽  
pp. 349-352 ◽  
Author(s):  
Javier Oliver ◽  
M. Caicedo ◽  
E. Roubin ◽  
A.E. Huespe
Keyword(s):  
Mesh Size ◽  
Cementitious Materials ◽  
Strong Discontinuity ◽  
Structural Failure ◽  
Multi Scale ◽  
Scale Modeling ◽  
Macro Scale ◽  
Numerical Tools ◽  
Continuum Strong Discontinuity Approach ◽  
The Continuum

This paper presents a FE2 multi-scale framework for numerical modeling of the structural failure of heterogeneous quasi-brittle materials. The model is assessed by application to cementitious materials. Using the Continuum Strong Discontinuity Approach (CSD), innovative numerical tools, such as strain injection and crack path field techniques, provide a robust, and mesh-size, mesh-bias and RVE-size objective, procedure to model crack onset and propagation at the macro-scale.

scholarly journals On the Fracture Models Determined by the Continuum-strong Discontinuity Approach

2006 ◽  
Vol 137 (1-4) ◽  
pp. 211-229 ◽  
Author(s):  
A. E. Huespe ◽  
J. Oliver ◽  
M. D. G. Pulido ◽  
S. Blanco ◽  
D. Linero
Keyword(s):  
Strong Discontinuity ◽  
Fracture Models ◽  
Continuum Strong Discontinuity Approach ◽  
The Continuum

scholarly journals The nature of the animacy organization in human ventral temporal cortex

eLife ◽  
2019 ◽  
Vol 8 ◽  
Author(s):  
Sushrut Thorat ◽  
Daria Proklova ◽  
Marius V Peelen
Keyword(s):  
Neural Networks ◽  
Convolutional Neural Networks ◽  
Temporal Cortex ◽  
Behavioral Experiments ◽  
Diagnostic Features ◽  
Social Agents ◽  
Independent Components ◽  
Ventral Temporal Cortex ◽  
Organizing Principles ◽  
The Continuum

The principles underlying the animacy organization of the ventral temporal cortex (VTC) remain hotly debated, with recent evidence pointing to an animacy continuum rather than a dichotomy. What drives this continuum? According to the visual categorization hypothesis, the continuum reflects the degree to which animals contain animal-diagnostic features. By contrast, the agency hypothesis posits that the continuum reflects the degree to which animals are perceived as (social) agents. Here, we tested both hypotheses with a stimulus set in which visual categorizability and agency were dissociated based on representations in convolutional neural networks and behavioral experiments. Using fMRI, we found that visual categorizability and agency explained independent components of the animacy continuum in VTC. Modeled together, they fully explained the animacy continuum. Finally, clusters explained by visual categorizability were localized posterior to clusters explained by agency. These results show that multiple organizing principles, including agency, underlie the animacy continuum in VTC.

scholarly journals Entropic Dynamics in Neural Networks, the Renormalization Group and the Hamilton-Jacobi-Bellman Equation

Entropy ◽  
2020 ◽  
Vol 22 (5) ◽  
pp. 587
Author(s):  
Nestor Caticha
Keyword(s):  
Neural Networks ◽  
Renormalization Group ◽  
Bayesian Learning ◽  
Bellman Equation ◽  
Depth Limit ◽  
Feed Forward ◽  
Hamilton Jacobi Bellman Equation ◽  
Hamilton Jacobi Bellman ◽  
The Continuum ◽  
Entropic Dynamics

We study the dynamics of information processing in the continuum depth limit of deep feed-forward Neural Networks (NN) and find that it can be described in language similar to the Renormalization Group (RG). The association of concepts to patterns by a NN is analogous to the identification of the few variables that characterize the thermodynamic state obtained by the RG from microstates. To see this, we encode the information about the weights of a NN in a Maxent family of distributions. The location hyper-parameters represent the weights estimates. Bayesian learning of a new example determine new constraints on the generators of the family, yielding a new probability distribution which can be seen as an entropic dynamics of learning, yielding a learning dynamics where the hyper-parameters change along the gradient of the evidence. For a feed-forward architecture the evidence can be written recursively from the evidence up to the previous layer convoluted with an aggregation kernel. The continuum limit leads to a diffusion-like PDE analogous to Wilson’s RG but with an aggregation kernel that depends on the weights of the NN, different from those that integrate out ultraviolet degrees of freedom. This can be recast in the language of dynamical programming with an associated Hamilton–Jacobi–Bellman equation for the evidence, where the control is the set of weights of the neural network.

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