Onscale, Inc.

Ansys, Inc.

Breakout Learning, Inc.

Joshua Oster-Morris

David M Freed

Scott McClennan

Ryan Matthew Diestelhorst

Ian Campbell

Ramit Varma

Kyle Kosic

Anil Sehgal

Steven Walters

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

This disclosure pertains to an innovative approach for enhancing monitoring multiparty stream communication analysis and machine learning applications. The apparatus monitor a multiparty stream communication, wherein the multiparty stream communication includes a first and a second packet series to generate, retrieve, and compare data elements. Training data is filtered and categorized into specific sub-populations, increasing relevance to particular subjects of analysis. These tailored datasets optimize machine learning models, resulting in more precise comprehension assessment.

Apparatus and method for monitoring multiparty stream communications

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.

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Atlanta, GA, United States of America

Joshua Oster-Morris