Triangulation of trimmed surfaces in parametric space

1992 ◽  
Vol 24 (8) ◽  
pp. 437-444 ◽  
Author(s):  
X. Sheng ◽  
B.E. Hirsch
Keyword(s):  
Parametric Space ◽  
Trimmed Surfaces

Approximate Degree Reduction of NURBS Curves and Surfaces: An Integrated Approach

1994 ◽  
Author(s):  
Srinivasa P. Varanasi ◽  
Athamaram H. Soni
Keyword(s):  
Shape Control ◽  
Data Exchange ◽  
Data Transfer ◽  
Integrated Approach ◽  
Free Form ◽  
Aircraft Industry ◽  
Trimmed Surfaces ◽  
Curves And Surfaces ◽  
High Degree ◽  
Boundary Curves

Abstract Data exchange between different CAD systems usually requires conversion between different representations of free-form curves and surfaces. Also, trimmed surfaces give rise to high degree boundary curves. Accurate conversion of these forms becomes necessary for reliable data transfer. Also important is the issue of shape control, specially in the aircraft industry. The objective of this paper is to investigate conversion methods and effect of shape control on the design and choice of such methods.

scholarly journals Hybrid Modeling of Materials Properties for Improved CAE-Simulations

2016 ◽  
Vol 854 ◽  
pp. 163-166 ◽  
Author(s):  
Uwe Diekmann ◽  
Alex Miron ◽  
Andreea Trasca
Keyword(s):  
Curve Fitting ◽  
Constitutive Models ◽  
Measured Data ◽  
Data Sources ◽  
Flow Curves ◽  
Mathematical Functions ◽  
Materials Properties ◽  
Parametric Space ◽  
Different Dimensions ◽  
Different Sources

The new MatPlus software supports the multi-dimensional modelling of materials properties using different data sources. Extensive mathematical functions allow curve fitting of data from different sources to any constitutive models and selectively combining models and datapoints along different dimensions. Physically consistent extrapolation of measured data within the complete multi-dimensional parametric space can be achieved. An integrated library of models can be extended by the user and already contains many popular equations like Hensel-Spittel and Zerilli-Armstrong for flow curves.

scholarly journals Interpolation of multidimensional signals using the reduction of the dimension of parametric spaces of decision rules

2019 ◽  
pp. 31-40
Author(s):  
M V Gashnikov
Keyword(s):  
Decision Rule ◽  
Optimization Problem ◽  
Decision Rules ◽  
Compression Method ◽  
Parametric Space ◽  
Archive File ◽  
Multidimensional Signals ◽  
Reduced Dimension ◽  
Hierarchical Compression ◽  
Adaptive Interpolator

In this paper, we consider the interpolation of multidimensional signals problem. We develop adaptive interpolators that select the most appropriate interpolating function at each signal point. Parameterized decision rule selects the interpolating function based on local features at each signal point. We optimize the adaptive interpolator in the parameter space of this decision rule. For solving this optimization problem, we reduce the dimension of the parametric space of the decision rule. Dimension reduction is based on the parameterization of the ratio between local differences at each signal point. Then we optimize the adaptive interpolator in parametric space of reduced dimension. Computational experiments to investigate the effectiveness of an adaptive interpolator are conducted using real-world multidimensional signals. The proposed adaptive interpolator used as a part of the hierarchical compression method showed a gain of up to 51% in the size of the archive file compared to the smoothing interpolator.

scholarly journals Synthesis of Cesium Lead Halide Perovskite Nanocrystals in a Droplet-Based Microfluidic Platform: Fast Parametric Space Mapping

Nano Letters ◽  
2016 ◽  
Vol 16 (3) ◽  
pp. 1869-1877 ◽  
Author(s):  
Ioannis Lignos ◽  
Stavros Stavrakis ◽  
Georgian Nedelcu ◽  
Loredana Protesescu ◽  
Andrew J. deMello ◽  
...  
Keyword(s):  
Space Mapping ◽  
Microfluidic Platform ◽  
Perovskite Nanocrystals ◽  
Parametric Space ◽  
Halide Perovskite ◽  
Lead Halide

A Novel Geometric Tolerance Modeling Inspired by Parametric Space Envelope

2018 ◽  
Vol 15 (3) ◽  
pp. 1386-1398 ◽  
Author(s):  
Chen Luo ◽  
Pasquale Franciosa ◽  
Darek Ceglarek ◽  
Zhonghua Ni ◽  
Fang Jia
Keyword(s):  
Geometric Tolerance ◽  
Parametric Space ◽  
Tolerance Modeling

scholarly journals Massive computational acceleration by using neural networks to emulate mechanism-based biological models

2019 ◽  
Vol 10 (1) ◽  
Author(s):  
Shangying Wang ◽  
Kai Fan ◽  
Nan Luo ◽  
Yangxiaolu Cao ◽  
Feilun Wu ◽  
...  
Keyword(s):  
Neural Network ◽  
Neural Networks ◽  
Mechanistic Model ◽  
Time Frame ◽  
Biological Applications ◽  
Biological Models ◽  
Parametric Space ◽  
Trained Neural Network ◽  
Short Time

Abstract For many biological applications, exploration of the massive parametric space of a mechanism-based model can impose a prohibitive computational demand. To overcome this limitation, we present a framework to improve computational efficiency by orders of magnitude. The key concept is to train a neural network using a limited number of simulations generated by a mechanistic model. This number is small enough such that the simulations can be completed in a short time frame but large enough to enable reliable training. The trained neural network can then be used to explore a much larger parametric space. We demonstrate this notion by training neural networks to predict pattern formation and stochastic gene expression. We further demonstrate that using an ensemble of neural networks enables the self-contained evaluation of the quality of each prediction. Our work can be a platform for fast parametric space screening of biological models with user defined objectives.

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