Complex engineering problems demand rigorous planning, systematic execution, and the coordinated use of specialized tools to deliver safe, reliable, and validated products under tight development schedules. Driven by recent advances in artificial intelligence and continued platform evolution at d3VIEW, we have integrated enterprise-grade Agentic Engineering Workflows (AEW) into the d3VIEW environment. These workflows are powered…
Accurately calibrating failure behavior in polymer materials often requires more than identifying how far simulations deviate from physical test results. Once failure strain error has been quantified, engineers must determine how to translate that information into controlled updates of material regularization behavior. d3VIEW addresses this need with the *GISSMO_SCALE_REGULARIZATION_SCALE_FACTORS worker, which enables automated, rule-based scaling…
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 to export this dataset automatically.…
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 includes (*INCLUDE, *INCLUDE_PATH). The biggest…
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 User menu where admins manage…
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 following inputs The output of…
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 matches the test is critical…
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…
