On the d3VIEW platform, there are many export options to manually export output datasets. Now, a new support is added to automatically export Workflow worker dataset output to Excel file upon execution. Export Dataset Within the Same Worker When a worker has a dataset output, we can configure the worker...

One of the core activities in virtual-product-development is compilation of a system-model, from assemblies, that can be evaluated across a wide range of load-cases. The traditional approach involves storing the assemblies in the form of files (solver inputs) on a storage device and then referencing them in a solver-main-input using...

Role based access in d3VIEW allows admin users to create custom roles. These custom roles can then be granted access to selected applications with specific permissions. Seen below is an example of sample roles: Creating and Accessing a New Role in d3VIEW The Roles option is available under the Administrative...

Many engineers use Python script to automate their tasks. Now we can integrate Python to d3VIEW Workflows. A new worker called Custom_Application can be used to run the Python script. The output files can be collected and used in workflow for further analysis, and reporting. Custom Application worker needs the...

Accurately determining and comparing failure strain between physical tests and simulations is a vital part of material calibration, validation, and iterative improvement of constitutive models.  Below image illustrates a single test (black) that requires to be compared with several simulations with varying degrees of correlation. Picking the simulation that best...

When we have a dataset on hand, it is often of our interest to run a quick analysis and get some insights. The analysis usually includes making predictions of a new dataset, grouping records, identifying important features, and optimization. We can achieve these goals by building a machine learning model...

NOTE: Robustness reported in this article is being revised with corrections in derivative calculations. Updated results will be published shortly from these corrections. Introduction A natural approach of understanding an unknown model is to get some sample points from the model and use these points to build a response surface...

Similar to the dataset_simulated_annealing_optimizer worker, which takes a dataset as input, we can consider the curve_simulated_annealing_optimizer worker. It takes a curve as input and returns the optimal y value of the curve as output. A curve can be considered as a dataset with two columns with each representing one coordinate...

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...

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...