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.

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.

VCollab’s product suite is a new class of tool that leverages digital 3D data to transform simulation post processing and report writing. This post takes a closer look at VCollab Pro.

Significant efficiency gains can be realized by appifying a repeated process used to simulate mild derivative designs. Integration and automation coupled with strict adherence to a design rule-based methodology ensures fast and reliable new designs.

Simcenter Mechanical tools let you model the complexity of your products, help your development processes go faster, explore more possible design options, and keep your simulation data and processes integrated.

Abstract Modeling as the easiest way to automate (CFD) pre-processing.

This paper outlines how CAENexus abstract modeling streamlines CAE processes to improve productivity and engineering collaboration.

Simufact Additive is a powerful and scalable software solution for the simulation of metal-based additive manufacturing processes. Discover how to use Simufact Additives to optimize your metal 3D printing / rapid prototyping.

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.

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

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.

In this webinar, you will learn how a fly-it-before-you-build-it approach can help aerospace manufacturers and their suppliers deploy a comprehensive digital twin for aircraft performance engineering.

Templates for fatigue analysis and their strength-only counterparts are relatively recent additions to Simcenter 3D. As a result, many engineers are unaware of them or the benefits that they can bring. In order to provide you with a practical example we revisited an old friend, to see how he was using the tools to his advantage.

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

In this conversation with Michele Del Mondo, Director Market Development Automotive at PTC, learn how PTC is helping manufacturers and suppliers in the automotive industry with their digital transformation.

There is valuable data in every company. This data often remains undetected or only known in part in different areas. If a company is looking for clues, the question arises as to which is the right tool in connection with the right methodology.

See how the Aras Simulation Management environment is created and reused, how it is related to items managed by Aras Innovator PLM platform, and how that platform’s change and configuration management helps to establish and maintain traceability.

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.