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How to Accelerate R&D with AI/ML-Based Simulation Featuring NVIDIA Modulus on Rescale

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.

AI and ML: The Brave New World of Simulation

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

SMBC Accelerates DataDriven Culture with AutoFE

When Sumitomo Mitsui Banking Corp (SMBC) – one of the world’s largest banks – wanted to accelerate their AI development practice, they evaluated thousands of vendors and short listed the best ones.

Solution Providers:

Simulation Vs. Machine Learning

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.

Big Compute 22: Convergence of Cloud, HPC, and AI Creates New Possibilities in Engineering and IT Operations

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.

Cooling an electric race car

Each year, the McGill Formula Electric (MFE) team designs and produces an electric racing car.  Using Simcenter made the task efficient and fast – a huge improvement over Excel grids and the Simulink model much less intuitive. The simulation is now easy to modify to evaluate other designs.

Solution Providers:

AI/ML Based Simulation of Customer Environment

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.

Webinar: The Simulation Platform for Applied AI

This recorded webinar presents simulation and AI concepts and technologies.

How AI and ML are Changing Simulation

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.

Scaling AI/ML Initiatives: The Critical Role of Data

In this white paper, IDC offers considerations for how organizations can address these challenges.

How to drive continuous innovation with digital twins

Through digital twins, new concepts, products, processes, value streams and environments can be visualized and shared digitally, and at a fraction of the cost of a real-world implementation, pilot or prototype.

Simulation in the Era of Big AI

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: Understanding more about data with less computing

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.

Role of Simulation in Machine Intelligence: Beyond Synthetic Data

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.

Simulation: Cutting the Corner on Machine Learning

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.

AI-Powered Engineering, Beyond Simulation

AI and HPC allow new, powerful systems that help engineers make sense of data, and complexity.

Tech Highlight Demo: Adams Real Time: Hardware-in-the-loop Solution

Real Time computational speed is a pre-requisite when combining software models with hardware components, such as a chassis stability controller, vision/range sensors or a driving simulator. MSC Adams has long been the automotive industry's tool of choice for vehicle dynamics predictions.

Augmented Reality in META

This short video from Beta CAE demonstrates Augmented Reality in the META post processor.

Combining Simulation and Machine Learning

This webinar shows how the different predictive abilities of simulation and machine learning combine to advance decision support in business and public enterprise.

Better Simulations will revolutionize Machine Learning

How simulations will solve the biggest problem in ML.

Time to Marry Simulation Models and Machine Learning

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”.

Machine Learning in Network Modeling and Simulation

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?

Are simulators effective at training heavy equipment operators? The answer is: Today’s best-in-class simulators are extremely effective. Here’s why.

How to put humans at the center of intelligent machine design with simulation

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.

Time to Marry Simulation Models and Machine Learning

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”.

Machine Learning in Network Modeling and Simulation

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?

Are simulators effective at training heavy equipment operators? The answer is: Today’s best-in-class simulators are extremely effective. Here’s why.

How to put humans at the center of intelligent machine design with simulation

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.

Fighting Fire with AI

Firefighting, arguably one of the world’s most dangerous professions, is becoming much safer thanks to AI.

Machine Learning applications in Engineering Simulation for the automotive industry

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.

Artificial Intelligence (AI) vs. Machine Learning vs. Deep Learning

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.

Simulation, AI, Optimization and Complexity

What Are Simulations and Why Are They Useful?

Trends of AI in industry part 2: Speeding up the mundane

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.

Trends of AI in industry part 1: Improving the user experience

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.

Data Science and Artificial Intelligence Applied in the Field of Materials & ICME

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.

Developing and Operating Large-Scale, Connected Digital Twins

AI, simulation, and customization are just some of the key components of enterprise digital twins.

AI Needs to Learn How to Play Fair

The NSF is granting millions of dollars to promote fairness, equity and ethics in AI. These research projects reveal why.

AI Is Automating Defect Inspection

New FPGA-based machine vision platform aims to be quicker and better than human defect inspectors.

AI and ML Transforming Simulation

Use of artificial intelligence and machine learning algorithms in FEA increase predictive performance, speed up processing time.

Space 4.0: A new era for the space industry

Software tools like Simcenter have enabled private and public organizations alike. Spacecraft designers have powerful tools to confidently deal with complexity, reduce costs and risks, and deliver excellence and safety. Download the white paper to find out what Space 4.0 holds in store and what you’ll need to compete.

BiW First Torsional Mode Prediction using Feature-Based Machine Learning

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.

Employing Machine Learning for Front Crash Responses Prediction

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.

Employing Machine Learning for Torsional Stiffness and Angle Prediction

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.

What Is the Role of AI in Simulation?

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.

AI Roadmap: Engineering Success with Data and Strategy

AI is no longer the stuff of science fiction. It offers concrete benefits in all areas of engineering, manufacturing, and operations. Artificial intelligence (AI) has an enormous potential to advance industries and change the way we work, live, and create.

Data Analytics & AI Services for Manufacturing

A lot of companies have slowly become “data rich” but they are still “wisdom poor,” as much of their collected data goes unused and un-managed. How can manufacturers move ahead with AI projects efficiently and successfully? This post covers a few of the key concepts for industrial AI in engineering and manufacturing.

Benefits and Challenges of Electrification & Digitalization

Electrification and digitalization are omnipresent in all aspects of modern life –from how we drink coffee to how our food is produced and to the complex behind-the-scenes processes of how we manufacture products.

Constant Change: An Industrial Engineering and Manufacturing Perspective

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.

AI-Readiness Assessment: Find out what the next step is towards your successful AI journey!

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

Tackling Design Complexity with Simplified Modelling Processes

Design engineers face daily challenges. These typically include slow CAE analysis, having to set up an analysis model for each new design iteration, siloed simulations that differ from real world product performance, and delays in moving from CAD to CAE.

Set Up and Run Multiphysics CFD Simulations Faster with Simcenter STAR-CCM+’s Latest Release

STAR-CCM+ can now serve your innovation team better than ever with its new 2022.1 release.

Shaky business? Random base excitation simulation offers accuracy and efficiency

This white paper takes a deep, technical dive into the pitfalls of random base excitation simulation, offering solutions to help you obtain accurate results efficiently, with limited computation time. Discover the keys to improve your random analysis workflow while reducing costs.

Vehicle Electrification Lunch and Simulate Webinar Series

This series of four webinars offers solutions to the challenges of next-gen electromechanical design.

How to Rapidly and Simultaneously Update Multiple Structural Dynamic Models

Simcenter 3D FE Model Update helps improve the fidelity of simulations. By adjusting the model’s material and physical properties parameters (called Design Variables), it is possible to produce a close match to actual product performance.

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