Simulated annealing Simulated annealing is an optimization method to find the global optimum of the objective function. It is inspired by the process of metal annealing which heats the metal to a very high level and cools down in a controlled manner. In the SA algorithm, a random point is selected to start with. A…
Given two curves, a baseline and a candidate curve, how do we know how similar they are to each other? In machine learning, it is common to compare the predicted values (candidate curve) of a testing set to the true values (baseline curve) by RMSE (Root Mean Square Error). By comparing RMSE values of different…
Machine learning techniques (https://www.d3view.com/introduction-and-application-of-d3view-ml/) are becoming unprecedentedly popular. And it plays an important role in data analysis. It is critical to find the model that demonstrates the best performance. Intuitively, we can build a few different models with the data given and see which model gives the best score, either it being RMSE (root mean…
d3VIEW has a new worker called ‘Workflow Provider’ which allows us to execute other workflows we may have saved on the platform within the current one we are building and executing. This saves us time from rebuilding parts of a workflow that can be taken from an already built one. Find the ‘Workflow Provider’ worker…
The new ‘Text Parameterize GUI’ worker helps to create machine generated designs by providing a simple to use interface to parameterize any part of a text file. Examples of such applications include single stage Design of Experiments, Sensitivity Analyses, Multi-stage Optimization and for generic machine learning problems. Locate the ‘Text Parameterize GUI’ worker under the…
Workflows is d3VIEW’s application for streamlining complex business processes. With its capabilities, we can create and employ automated DOE studies for quicker analysis. Using the application’s new shape worker, *TEXT_PARAMETERIZE_GUI, we can set-up custom parameters for our text-based files, generate sampling points from these parameters, create simulations from the text, launch the DOE studies from…
Introduction Given a set of points, we want to construct a curve that shares some specific patterns described by a group of curves. From the description of the problem, there are two pieces of information available to us. First, the points available (Prediction Points) are part of an unknown curve we want to reconstruct. Second,…
It is not unusual that due to various limitations, researchers can only collect limited number of samples. Meanwhile, for many analyses, we desire a higher resolution. In two-dimension case, we have X and Y coordinates of our points. We are interested in what happens in between any of the two points. We can draw a…
Battery data have many interesting properties. One of them is cycles. When we consider the whole range of the data, there are a few of the metrics we are interested in. For example, test net capacity and energy, test cumulative capacity and energy, etc. From these transformed curves, we can obtain some metrics that help…
As simulations drive a large portion of product development, reliance on test-data at critical development stages is invaluable to further our understanding of reality and assess our simulations. Among a wide variety of tests conducted today, the test-data can be broadly grouped into coupon-based (for material calibration), component based (for sub-system validation) and full-system tests.…
