This video tutorial discusses the relationship between SolidWorks and Artificial Intelligence.
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.
When used together, AI expands the value of simulation by answering questions and solving problems that typically require extensive engineering hours to discover.
There are some broad implications for the future of the composable enterprise and for building more trustworthy AI.
Newest release democratizes simulation with AI and improved data and workflows.
Cloud-based AI in space can identify damage to gear, process health data and test 3D printed parts.
Pritish Shubham, Altair Ambassador provides an Introduction to Computer-Aided Engineering in this Machine Learning and CAE Video.
This blog presents an approach to real-time Computational Fluid Dynamics, CFD, (and real-time CAE, Computer-Aided Engineering) that delivers repeatable engineering-level fidelity suitable for engineering simulation analysts.
This article uses a novel AI-based application, romAITM, to generate a Reduced-Order-Model (ROM) from a large database of static simulations and optimize their parameters. We will demonstrate how this innovative approach can support the design of an Aircraft Wing.
Alien intelligence, or a tool to take over engineering drudge work? Depends who you ask.
The variety of programs and courses reveal the field is wide open.
AI, machine learning, and other digital tools are changing what’s possible for architecture. Bill Allen shares his thoughts.
Though simulation, AI, and HPC are now arriving at the forefront of golf club development and design across the board, Altair has been helping some of golf’s biggest manufacturers make better equipment for years.
An interview with Pierre Baqué, founder and CEO.
Why not combine the human ability to Infer the optimum shape into topology optimization?
The most advanced AI today doesn’t put much faith in human intuition. It should.
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.
NVIDIA Modulus, previously referred to as SimNet, is a framework for developing physics machine learning neural network models.
AI and HPC allow new, powerful systems that help engineers make sense of data, and complexity.
Generative design is a design exploration process that uses AI to create a wide range of solutions and ideas for complex problems. Will AI take over the design world as we know it?
The objective of this survey is to gain a granular understanding of the adoption of simulations in R&D processes today, and to examine where this trend is likely to head in the future.
In this webinar, engineering experts Thomas von Tschammer (Neural Concept) and Anthony Massobrio (Intelligent Simulation) present a practical Automotive Use-Case.
This document contains a practical and comprehensive introduction of everything related to deep learning in the context of physical simulations.
There are some important steps to exploit the full potential of 3D Deep Learning (but also of any machine learning technique): preparing your data so that the model can extract the maximum of information out of it.
This article provides a high-level overview of multi-objective global function optimization and the benefits one can unlock utilizing deep learning approaches in constructing the surrogate model used in the optimization process.
A revolutionary approach is transforming the way companies approach tough engineering optimization challenges.
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).
In this video we demonstrate real-world cases how Chat GPT generated script can be directly used for SysML simulation.
AI and simulation increasingly work together to enable more versatile and adaptive systems. Data and analytics leaders should combine these technologies to improve the applicability of machine learning, to enable more sophisticated decision intelligence and to accelerate business optimization.
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.
Within the research group AI-augmented Simulation numerical methods and simulation tools for solving engineering problems are enhanced by artificial intelligence (AI) approaches and machine learning techniques.
Digital twins of cities can help accelerate AI development for the automotive industry and beyond.
NASA is creating AI-designed parts while the European Space Agency searches for life-friendly planets using AI.
As artificial intelligence becomes more important to engineering, industry needs better ways to create and manage all the new data generated. Altair has taken recently acquired products in this sphere and combined them into one efficient package it calls RapidMiner. Keep reading to learn why this new platform is Digital Engineering 24/7 Editor’s Pick of the Week.
Using artificial intelligence and machine learning, the time to set up workflows and run simulations is reduced.
In this Machine Design 24/7 article, Ansys CTO says that he expects reduced order models to become part of simulation.
Artificial Intelligence (AI) is leveraged to enhance the Additive Manufacturing outcome. AI efficiently detects statistical patterns in each set of data and simplifies complex studies with reduced-order and surrogate modelling. In addition, the AI model establishes the link between the input parameters and the output performance, enabling an optimization loop.
Engineering has always been considered an evolving industry. The increase of AI technologies enables engineers to complete their work more efficiently and solve a wider range of problems, empowering their own expertise, and making them the main actors of future’s development and success.
At OzenCon, Ansys CTO Dr. Prith Banarjee discusses how AI programs like ChatGPT might change engineering simulation. NVIDIA and Dell discuss the processing power and hardware needed to run AI or Machine Learning workloads.
Artificial Intelligence (AI) and Simulation are two very familiar terms in the Tech world – but how do the pair partner up?
Simulation and artificial intelligence (AI) are two very different options for simulating reality using mathematical methods. Until now, there have been very few joint approaches, but successful current projects are revealing outstanding new opportunities for combining the two technologies.
Intel Xeon W CPUs, Intel Xeon Scalable CPUs, and NVIDIA RTX 6000 GPUs are at the core of the new AI Workload-targeted Machines.
This Special Focus Issue of Digital Engineering highlights the new technologies automakers are bringing to bear on their toughest design challenges. Topic areas include simulation, 3D printing, autonomous driving, and design.
Thanks to advances in artificial intelligence (AI) and machine learning (ML), it is possible to gather and analyze design knowledge.
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.
Exactly where does AI fit into the design and development landscape? Does it replace physics-based simulations, or are the two complementary toolsets?
Many organizations are improving their simulation capabilities by incorporating artificial intelligence (AI) into their model-based design. Historically, these two fields have been separate, but create significant value for engineers and researchers when used together effectively. These technologies’ strengths and weaknesses are perfectly aligned to help businesses solve three primary challenges.
Learn how Artificial Intelligence with specific Machine Learning models can reduce costs and improve the quality of products and services.
The automotive industry faces challenges; design teams in automotive companies are under pressure. Can artificial intelligence (AI) help product design teams in the automotive industry? How can supply chains become more efficient with AI and data science?
Incorporating advanced technologies such as AI and data analytics into their design processes can help mechanical engineers leverage large amounts of data. Focusing on upfront mechanical design, we dive into the topics of AI and Data Science for mechanical engineers and show the up-and-coming applications of Deep Learning to 3D simulation with three use cases.
This blog post details how an AI-powered generative design tool, ColdStream, can significantly reduce engineering time. Let’s go over more details about each one of the approaches.
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.
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.
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.
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.
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.
Are simulators effective at training heavy equipment operators? The answer is: Today’s best-in-class simulators are extremely effective. Here’s why.
Firefighting, arguably one of the world’s most dangerous professions, is becoming much safer thanks to AI.
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.
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.
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.
AI, simulation, and customization are just some of the key components of enterprise digital twins.
The NSF is granting millions of dollars to promote fairness, equity and ethics in AI. These research projects reveal why.
New FPGA-based machine vision platform aims to be quicker and better than human defect inspectors.
Use of artificial intelligence and machine learning algorithms in FEA increase predictive performance, speed up processing 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.
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.
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
This webinar highlights the benefits of using artificial intelligence in the design of vehicle crash structures as well as occupant kinematics in future autonomous vehicles.
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.
Orbital Stack, the CFD tool created by global leaders in wind engineering and microclimate analysis, RWDI, partnered with Engineering Intelligence firm, Neural Concept, in order to add the power of AI to their App for structural engineers. Orbital Stack makes climate-informed design easy, affordable, and understandable.
