Large eddy simulations LES based on the Smagorinsky model can be conveniently used in the lattice Boltzmann method LBM because the strain rate tensor, Sij , used to determine the eddy kinematic viscosity can be calculated from the second-order moment of the nonequilibrium distribution function, and the current total nondimensional relaxation time can be determined explicitly. A new method is de...

This paper introduces a probabilistic framework to estimate parameters of an acquisition function given observed human behavior that can be modeled as collection sample paths from Bayesian optimization procedure. The methodology involves defining likelihood on task, where the is parameterized by subroutine governed unknown function. structure enables us make inference subject's while allowing t...

In sheet metal forming processes with complex strain paths, a part is subjected to large plastic deformation. This severe plastic deformation leads to high plastic strain localization zones and subsequent accumulation of those strains. Then internal and superficial micro-defects and in other words ductile damage is created. This damage causes quality problems such as fracture. Therefore, design...

Local causal structure learning aims to discover and distinguish direct causes (parents) effects (children) of a variable interest from data. While emerging successes have been made, existing methods need search large space target T. To tackle this issue, we propose novel Efficient Causal Structure algorithm, named ELCS. Specifically, first the concept N-structures, then design an efficient Mar...

Recently, short fiber reinforced composites are widely utilized in many structural parts in automobile, infrastructure and electric equipment applications due to their high specific strength, high specific stiffness, long fatigue life and high impact resistance as well as cost effective capability of production. If these parts are exposed to low-velocity impact, significant damages may develop ...

An adaptive inteTpTeter for a programming language adapts to particular applications by learning from execution experience. This paper describes PROLEARN, a prototype adaptive interpreter for a subset of Prolog. It uses two methods to speed up a given program: explanation-based generalization and partial evaluation. The generalization of computed results differentiates PROLEARN from programs th...

Large-scale problem solving algorithms will prove to be critical in implementing the long-term vision of cyberphysical systems (CPS). Such algorithms will be highly modularized -that is, they will use subroutines implemented behind abstract interfaces. A characteristic of many problem solving algorithms is that their performance varies wildly depending on which order subroutines are executed, w...

We present an approach to map Robocup-soccer narratives (in natural language) to a sequence of meaningful events. Our approach takes advantage of an action-centered framework, an inference subroutine, and an iterative learning algorithm. Our framework represents the narrative as a sequence of sentences and each sentence as a probability distribution over deterministic events. Our learning algor...

A new application programming interface for evaluating functions and specifying optimization problems at runtime has been developed. The new interface, named FEFAR, uses a simple language named LEFAR. Compared with other modeling languages such as AMPL or OSil, LEFAR is Fortranlike hence easy to learn and use, in particular for Fortran programmers. FEFAR itself is a Fortran subroutine hence eas...

This paper proposes and analyzes an accelerated inexact dampened augmented Lagrangian (AIDAL) method for solving linearly-constrained nonconvex composite optimization problems. Each iteration of the AIDAL consists of: (i) inexactly a proximal (AL) subproblem by calling gradient (ACG) subroutine; (ii) applying under-relaxed Lagrange multiplier update; (iii) using novel test to check whether pena...