Onscale, Inc.

Ansys, Inc.

Scott McClennan

David M Freed

Ryan Matthew Diestelhorst

Ian Campbell

Joshua Oster-Morris

Jeff Dobson

Jonathan McLaughlin

Laura Carcione

Josh Oster-Morris

Andy Tweedie

Gerald Harvey

Robbie Banks

Kyle Kosic

Anil Sehgal

Estimating the computational cost of simulation using a machine learning model. An example method includes inputting a feature data set into a machine learning model. The feature data set includes model geometry metadata and simulation metadata. The method further includes predicting, using the machine learning model, a computational cost characteristic for a simulation process.

Methods and systems for the estimation of the computational cost of simulation

Systems and methods for automated resource allocation during a computational simulation are described herein. An example method includes performing a simulation with a first set of computing resources. The method also includes dynamically analyzing at least one attribute of the simulation to determine a second set of computing resources for performing the simulation, and performing the simulation with the second set of computing resources. The second set of computing resources is different than the first set of computing resources.

Dynamic resource allocation for computational simulation

Systems and methods for automated resource allocation during a computational simulation are described herein. An example method includes analyzing a set of simulation inputs to determine a first set of computing resources for performing a simulation, and starting the simulation with the first set of computing resources. The method also includes dynamically analyzing at least one attribute of the simulation to determine a second set of computing resources for performing the simulation, and performing the simulation with the second set of computing resources. The second set of computing resources is different than the first set of computing resources.

Systems and methods are provided to move the solving of multi-physics engineering simulations away from specific CAE, or combination CAD and CAE, applications. In one embodiment, an Application Programming Interface (API) is provided that can be integrated into any device, system, application, or software workflow. The API exposes a series of functions or modules that a user can use to create a simulation file that includes parameters such as a model for the simulation, physics for the simulation, timings for the simulation, and other parameters. The simulation file may then be executed on one or more nodes of a cloud-based computer cluster, and the results of executing the simulation can be provided back to the user. The user may then visualize the results using their preferred device, software, application, or workflow.

Systems and methods for running a simulation

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Sunnyvale, CA, United States of America

Scott McClennan