A validated machine learning workflow for Li-ion battery crashworthiness. The trained predictor was reused after the design changed, with no new training data.
JuliaHub is the enterprise platform behind the Julia language ecosystem, trusted by engineers and scientists building high-performance, production-grade models across aerospace, automotive, energy, manufacturing, and semiconductors.
This synthetic podcast is based on Youngsoo et al.’s important paper, which strives to clarify use cases for ML surrogates in the ‘pre-trained’ versus’ foundation’ conversation.
Did you miss it? Explore these video sessions on next-gen user experience, crash & safety simulation, AI and ML applications, advanced modeling techniques, and real-world engineering applications from the experts.
This blog explores the similarities and differences between Machine Learning Engineers and AI Engineers.
This webinar covers compelling AI/ML-driven use cases and how they fit together in different scenarios: PLM, CAD, component and system level simulation and optimization, and physical test solutions.
The hype isn’t going anywhere, but what about the products? It’s still a game of wait-and-see.
ML enables packaging manufacturers to understand the impact of design changes much faster than traditional simulation methods allow. AI-enabled unified modeling and simulation (MODSIM) accelerates product development in the consumer-packaged goods & retail industry.
How is Artificial Intelligence (AI) and, more specifically, Machine Learning revolutionizing engineering processes in mechanical engineering?
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.
AI and ML technologies are compelling in identifying patterns and developing predictive models from large amounts of data. When exposed to AI and ML techniques, product and process data from an enterprise’s digital thread can transform the product lifecycle. Listen to the replay of this webinar recording.
Siemens' HEEDS AI Simulation Predictor unlocks new possibilities for manufacturers by empowering engineering teams to harness the potential of advanced AI-driven predictive modeling.
In this book, Dr. Justin Hodges shares his experience in facilitating AI/ML adoption in the engineering simulation domain. Topics include a simulation-specific machine learning pipeline, how to define CAE related problems for successful ML projects, modern trends, and a step-by-step learning pathway to build a compelling portfolio in AI/ML for engineering.
In the rapidly evolving landscape of product development, simulation has emerged as a cornerstone technology, driving innovation across critical sectors such as automotive, aerospace, defense, telecommunications, medical devices, and consumer products.
In this book, Dr. Justin Hodges shares his experience in facilitating AI/ML adoption in the engineering simulation domain. Topics include a simulation-specific machine learning pipeline, how to define CAE related problems for successful ML projects, modern trends, and a step-by-step learning pathway to build a compelling portfolio in AI/ML for engineering.
Learn how machine learning in predictive maintenance can offer benefits such as cost savings, increased reliability, extended equipment life, and customer satisfaction.
Introduction Machine learning presents many chances to further the subject of computational fluid dynamics and is quickly emerging as a fundamental tool for scientific computing.
This e-book provides a step-by-step guide to help you plan, develop and deploy your ML projects at scale.
Altair's approach is focused on democratizing AI for easy adoption and to increasing end user productivity in a secure, transparent & governed environment.
Researchers recently released a paper describing a new model that adds deep learning to earthquake forecasting.
As new digital technologies mature, the technical outlook for engineering design is robust, exciting and with much to offer the world around us.
This paper reviews several algorithms, with a focus on machine learning methods, to systematically tackle the three main stages of the additive manufacturing process.
This paper reports the performances of NCS on the problem of thermal simulations for satellite panel and discusses the potential impact on the design workflow.
This webinar recording features experts from Microsoft, Altair, and Neural Concept. See practical, real-world examples of the use of AI and Machine Learning to improve the simulation process.
This Engineer Innovation podcast explains how engineers can start using AI and ML to quickly achieve massive productivity savings and extra insight into simulations.
This roundtable discussion explores areas where AI and ML are expected to usher in radical changes.
This video demonstrates an Agent-Based Model with Reinforcement Learning for Autonomous Fleet Coordination.
Artificial intelligence and machine learning are affecting just about every part of our professional and personal lives — and engineering simulation is no exception.
Better, faster decisions are made possible when these transformational sciences align.
Artificial Intelligence and Machine Learning are becoming an even bigger part of the Additive Manufacturing process.
Artificial intelligence is no longer the stuff of science fiction. It’s not even some distant future technology that may or may not come to fruition.
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).
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.
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.
Artificial intelligence (AI), machine learning and deep learning are three terms often used interchangeably to describe software that behaves intelligently. However, it is useful to understand the key distinctions among them.
Use of artificial intelligence and machine learning algorithms in FEA increase predictive performance, speed up processing time.
The convergence of mechatronic and system-based engineering with advances in data management, artificial intelligence (AI), machine-learning (ML) and increasingly connected manufacturing is challenging traditional industrial product development and manufacturing processes.
This white paper discusses how synthetic datasets for training AI can be generated in hours using the OnScale cloud simulation platform. The demonstrated approach of using synthetic datasets to train AI networks can drastically reduce cost, risk, and time for the development of new hardware technologies.
CINCINNATI, OH; SEPTEMBER 5, 2024 – Revolution in Simulation (rev-sim.org), the global engineering simulation industry collaboration and technology alliance, has … Continue reading New Rev-Sim Learning Program on the use of Artificial Intelligence in Engineering Simulation
Artificial intelligence (AI) is rapidly emerging as a key enabler of innovation. What does this mean for design, particularly when … Continue reading Can AI Boost Design for Additive Manufacturing?
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
In this online discussion, a panel of experts fielded questions on the application of AI/ML technologies in Product Development and Simulation.
This panel discussion outlines strategic approaches for integrating AI/ML into CAE, with a focus on real-life experiences and insights from industry experts. Topics include CAE simulations, virtual animation data, and product design iterations and optimization.
Co-hosted by Rev-Sim, CIMdata, and Digital Engineering 24/7, this roundtable includes experts from Microsoft, Altair, and Neural Concept. Watch to learn their thoughts and see practical, real-world examples of the use of AI and Machine Learning to improve the simulation process while also highlighting the potential pitfalls of this approach.
Machine Learning Models Built in Altair AI Studio Display Real-time Data in Altair Panopticon Dashboards
For AZTEC, optimization using simulation provides a significant advantage to for reclaiming asphalt product. Watch this video to learn more.
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.
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