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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.
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Artificial intelligence and machine learning are affecting just about every part of our professional and personal lives — and engineering simulation is no exception.
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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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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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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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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.
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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.
