By using a machine learning model trained for a particular chip, it’s possible to cut through the noise in the resulting data and match different data points to particular failure modes, be it manufacturing defects or problems with the process itself.
AI, machine learning, and other digital tools are changing what’s possible for architecture. Bill Allen shares his thoughts.
Creativity and innovation are often limited by time constraints and the size and volume of simulations required to meet this demand. Artificial intelligence (AI) and machine learning (ML) can help clear this bottleneck.
Demystify machine learning through computational engineering principles and applications in this two-course program from MIT xPRO
Unlike traditional calibration, SmartUQ’s statistical calibration considers the discrepancy between the simulation and physical results which reduces bias and increases the model’s accuracy.
Many engineering simulations contain multiple inputs, making it critical for the engineer to identify the importance of each input to the overall model. This issue can be addressed by using Sensitivity Analysis (SA).
This paper provides an overview of how AI/ML-enabled software can improve design workflows, while also addressing common questions and concerns around implementing this technology in an engineering organization.
In this Machine Design 24/7 article, Ansys CTO says that he expects reduced order models to become part of simulation.
Machine learning is currently a popular concept in the technology world, and when combined with simulation, it is a valuable tool for product development.
With Neural Concept Shape, you can use 3D numerical simulations as input to train your deep learning models. If we take the example of aerodynamic simulations, these CFD simulations results are usually much larger files than images or text (a single result can reach several hundreds of GB). Hence storing a large amount of them can become an issue in the long term, as it would require to regularly scale up the hardware infrastructures accordingly.
Discover how to harness the power of ML-Agents, Unity Computer Vision and Robotics Simulation. Learn how Unity can train intelligent agents, generate synthetic images, and test and train robots to help you create smarter products.
This presentation explains the various uses of simulation in artificial intelligence as well as its importance. It was a follow-up to his incredibly popular simulation and AI integration insights.
Software tools increasingly include artificial intelligence (AI) and machine learning (ML) functionality to help automate some of the basic design exploration work, identify a wider range of possible options and help designers make better decisions faster. This issue presents these new AI capabilities, as well as other ways in which simulation and CAD software has been made easier to use and more accessible to a wider range of professionals.
We are starting to see AI applied to simulation everywhere. Although still early in the adoption cycle, AI for R&D has the potential to dramatically revolutionize how R&D works.
Artificial intelligence and machine learning are affecting just about every part of our professional and personal lives — and engineering simulation is no exception.
Simulation and machine learning are related in that they both revolve around models, but they are very different. In fact, simulation and machine learning are almost opposites. This article dives into the differences between the two and how they are used.
Yesterday the Big Compute 22 virtual event gathered the world’s pioneers in computing-driven innovation including both the technology providers and the practitioners solving the biggest challenges of our time. Rescale and other participants took the stage to showcase new features, capabilities, and industry advancements that are accelerating the pace of innovation.
This video includes an innovative AI/ML based approach that simulates disk storage system customer environment for product qualification. Solution includes an automated framework that fetches customer system telemetry data and performs analysis to create ML models.
This recorded webinar presents simulation and AI concepts and technologies.
AI/ML will help us narrow the gap between the ideal world (where time, effort, efficiency and results are perfectly balanced), and what happens in real life. It will enable us to make simulation productivity, ease of use and accuracy a little less of a trade-off.
In this white paper, IDC offers considerations for how organizations can address these challenges.
The Applied Machine Learning Days channel features talks and performances from the Applied Machine Learning Days. AMLD is one of the largest machine learning & AI events in Europe, focused specifically on the applications of machine learning and AI, making it particularly interesting to industry and academia.
This paper provides an overview of the use of physics-based simulation models to test, correct, and retest ML algorithms under a range of scenarios and at a scale not practicable with physical testing.
This webinar shows how the different predictive abilities of simulation and machine learning combine to advance decision support in business and public enterprise.
How simulations will solve the biggest problem in ML.
In this blog, the author says that the main reason for the divide between ML and Simulation is this: simulation models are built “process-centric” while ML models are built “data-centric”.
The integration of Machine Learning (ML) in network modeling and simulations is key to evaluating ML-based solutions and algorithms used to configure and optimize networks. In addition, data generated from simulations can be used to train and evaluate ML models, thus accelerating the design process and ensuring reliable comparisons with proposed solutions, whether they are based on ML or not.
Are simulators effective at training heavy equipment operators? The answer is: Today’s best-in-class simulators are extremely effective. Here’s why.
The future of machine design is coalescing around a number of trends, including self-driving vehicles, teleoperation of robots, and remote human assistance, across essentially all industrial domains.
Recent advances in Machine Learning (ML), makes it possible to bypass the assessment derived by contemporary FE analysis by utilizing legacy data. As a result, ML increasingly gains grounds as a powerful alternative method in the early stages of product design, liberating further innovation in engineering.
Use of artificial intelligence and machine learning algorithms in FEA increase predictive performance, speed up processing time.
The first torsional mode frequency value is a key value for concept cars. Having a trained algorithm, which provides answers based on a Finite Element Body-in-White model, without running a solver analysis, accelerates the digital model validation process.
Front crash is one of the most common finite element analyses during vehicle development. The Machine Learning functionality implemented in KOMVOS can be trained to predict the behavior of theoretical designs in a front crash, without running the full FE analysis. This enables the performance of multiple “what-if” studies without requiring the otherwise additional design and solver run time.
Torsional stiffness and torsional angle are among the most important key values in a vehicle’s Body in White (BiW) development. Using a trained Machine Learning model the identification of these values can be predicted in a fraction of the time needed for re-designing and running again the analysis.
In this Rev-Sim Guest Post, OnScale CTO, Dr. David Freed shares his views on the coming decade. Freed believes that engineering simulation will be hugely impacted by, and become inextricably entwined with, machine learning / artificial intelligence (ML/AI). Do you agree? Read all about it in this Rev-Sim Guest Post!
Press Release For Immediate Release CINCINNATI, OH (USA); MAY 15, 2023: Revolution in Simulation (Rev-Sim), created to accelerate innovation using … Continue reading Revolution in Simulation Expands Focus to Cutting-Edge Simulation
The drive towards full electrification of the car is an opportunity but also a challenge. Certain elements of the drivetrain will disappear, such as exhaust systems. On their turn, components such as the battery are now becoming prevalent.
