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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.
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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.
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This recorded webinar presents simulation and AI concepts and technologies.
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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.
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In this white paper, IDC offers considerations for how organizations can address these challenges.
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A small number of organizations have been developing AI models based on extremely large deep neural networks. We've found many important applications for these models, particularly in language processing and image analysis. But it could be that the most important applications over the coming decade will be in problems relevant to the simulation of complex physical systems.
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AI-enriched Simulation accelerates the discovery process by using AI to identify the most promising simulations to run on a massive data set. Just as importantly, it determines the computing infrastructure best suited for the task—whether that’s a basic calculator or even, a Quantum computer.
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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.
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Are simulators effective at training heavy equipment operators? The answer is: Today’s best-in-class simulators are extremely effective. Here’s why.
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Firefighting, arguably one of the world’s most dangerous professions, is becoming much safer thanks to AI.
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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.
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What Are Simulations and Why Are They Useful?
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Part 2 of this series explains the ways AI is speeding up and automating slow and mundane tasks, driving more efficient workflows and helping engineers and designers get back to doing what they were trained to do.
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AI-capabilities are emerging across a range of design and simulation solutions with Siemens digital industries software. This series explores different trends beginning with the way AI is reshaping the user experience of complex tools.
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Manufacturers are looking for ways to increase their agility and resilience, to become more responsive to supply issues and shifts in customer demand.
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This paper explores how generative thermal design and metal AM can be used in parallel to increase efficiency in semiconductor fabrication and boost the speed at which these vital components can be produced.
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Accelerate innovation, accelerate generation of material data, make substantial savings! This white paper unleashes the power of Data Science and Artificial Intelligence in the field of Materials and Integrated Computational Materials Engineering.
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AI, simulation, and customization are just some of the key components of enterprise digital twins.
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The NSF is granting millions of dollars to promote fairness, equity and ethics in AI. These research projects reveal why.
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New FPGA-based machine vision platform aims to be quicker and better than human defect inspectors.
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Use of artificial intelligence and machine learning algorithms in FEA increase predictive performance, speed up processing time.
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What are you doing to advance innovation? Here's an idea - bring artificial intelligence (AI) to bear on your product design process with Generative Design. Read this blog post to learn more.
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The term “AI” has been freighted with meanings in the popular imagination long before realizing practical uses. Now that AI is finally showing its value, we can examine what it means in manufacturing and engineering, specifically for uses in simulation.
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Change Notice, a weekly video podcast hosted by Instrumental Inc., seeks to address the topic in conversation with product design engineering leaders. In a recent episode, Maya HTT’s Remi Duquette joined host Anna-Katrina Shedletsky for a deep dive into the changes he’s seen over the last 10+ years.
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Answer this survey to understand your AI maturity level, and equally important, get the resulting report to ensure you take the right next step towards successful AI operations.
Typical Stakeholder: VP, Director, Manager for Maintenance, Operations, Plant, Fleet, and Service management
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How can discrete and continuous manufacturing take full advantage of rising digital technologies? Will the deployment of machine learning succeed, even in “dirty” industrial environments?
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This paper unleashes the power of Artificial Intelligence (AI) and Data Science (DS) in the field of Materials and Integrated Computational Materials Engineering (ICME). It develops leading edge applications, such as how to accelerate the generation of material data, enrich material databases, optimize manufacturing & design, among many other innovations that are now made possible with DS/AI.
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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.
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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.
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Engineering.com's Roopinder Tara asks: Can artificial intelligence help us with simulation?
