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How AI Improves Simulation Technology

Learn about the role of simulation in artificial intelligence (AI), machine learning (ML), and deep learning applications and how Ansys is integrating AI and ML into simulation software.

Benefits of Combining Artificial Intelligence and Simulation

Accelerate Speed and Accuracy

Ansys is using AI/ML methods to automatically find the simulation parameters to improve speed and accuracy simultaneously.

Augment Simulation

AI/ML can accelerate chip thermal solutions and develop a fluids solver that combines high-fidelity solutions in local regions with ML methods in coarse regions. 

Gain Business Intelligence

Drive business intelligence decisions such as compute resource prediction needs for Ansys simulation solvers. 

Optimize Design Space Exploration

AI/ML can guide early product optimization efforts to help engineers quickly find the best design space based on thousands of parameters. 

Accelerate Machine Learning with Simulation

Learn how Ansys Fluent can make effective use artificial intelligence (AI) to improve performance without compromising accuracy. Initial results show an 86X speedup.

Michael P. Brenner is the Michael F. Cronin Professor of Applied Mathematics & Applied Physics and a Professor of Physics at Harvard University. Brenner is also a Research Scientist at Google Research. He presents an overview of his work with Ansys and Google Research in “Machine Learning Convective Discretizations through User-Defined Functions in Fluent.”

Accelerate machine learning with simulation
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Engineering Simulation Applications for Artificial Intelligence 

AI/ML technology is applied successfully to numerous industries, such as natural language understanding for smart agents, sentiment analysis on social media, algorithmic trading in finance, drug discovery, and recommendation engines for electronic commerce.

People are often unaware of the role AI/ML plays in simulation engineering. In fact, AI/ML applies to simulation engineering and is critical to disrupting and promoting customer productivity. Advanced simulation technology, enhanced with AI/ML, is underpinning the engineering design process. 

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Culture Clash: The Human Side of AI and the IoT

Learn what happens when you integrate today’s most important technological innovations — artificial intelligence and the internet of things — with engineering simulation.

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Understanding Machine Learning for Materials Science

Machine learning dramatically decreases the time it takes to develop stronger, lighter materials. This is important to the automotive, aerospace and construction sectors. 

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How AI and ML are Changing Simulation

How AI and ML are Changing Simulation

See how Ansys is exploring the use of artificial intelligence/machine learning (AI/ML) to solve all of these problems. 

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How Ansys Virtually Verifies Autonomous Perception Systems

Why Autonomous Vehicles Need Thermal Cameras

FLIR’s Speos simulations demonstrate the utility of thermal cameras to the autonomous vehicle market. The images they produce can also be used to help engineers train artificial intelligence systems to distinguish living things from inanimate objects. 

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A New Kind of Eyes on the Road

Radar and 5G startup Metawave built a breakthrough radar platform, Integrated with Metawave’s AWARE artificial intelligence (AI), using Ansys HFSS to help overcome autonomous driving challenges. 

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Honda Motor Improves Development Efficiency with a Materials Database

Materials informatics is an efficient materials development method that integrates materials data with machine learning, unlike conventional trial-and-error materials development.

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

Artificial intelligence as a concept to describe a program that can sense, make decisions, act on them, and adapt based on the outcome of those decisions has been around at least since the first computers.

Machine learning is a means of realizing AI by providing algorithms with classified data so that they can improve over time without being explicitly programmed.

Deep learning, as a means to realize ML, uses artificial neural networks, which are algorithms that attempt to imitate how human brains make decisions, including making their own classifications of data. DL typically requires massive amounts of data and and high-performance computing (HPC).

Anywhere enough data can be collected to train algorithms is a ripe for AI development, from guiding autonomous vehicles to predicting energy usage to accelerating enigneering simulation by learning complex physics. 

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Engineering Autonomous Vehicles with Simulation and AI

Artificial Intelligence

Developing autonomous vehicle technology is a formidable challenge that requires new developments in sensing technologies, machine learning and artificial intelligence.

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Developing advanced driver assistance systems (ADAS) and autonomous vehicles is a challenge without precedent. Estimates indicate that billions of miles of road testing will be necessary to ensure safety and reliability. This impossible task can only be accomplished with the help of engineering simulation. With simulation, thousands of driving scenarios and design parameters can be virtually tested with precision, speed, and cost economy.

This 60-minute webinar will describe six specific areas where simulation is essential in the development of autonomous vehicles and ADAS. It will also provide examples and substantiate the benefits of simulation while identifying the tools needed for ADAS and autonomous vehicle simulation. 

Accelerate Simulation with Artificial Intelligence, Machine Learning and Deep Learning

AI enables engineers to work with large, complex designs more quickly without sacrificing accuracy for speed. 

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100X Simulation Speed Increase

Ansys uses deep neural networks within the Ansys RedHawk-SC family of products to speed up Monte Carlo simulations up to 100 times to better understand the impact of voltage on timing.

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1,000X Faster Solution

An automotive customer took advantage of Ansys OptiSLang’s machine learning techniques to find an autonomous solution to the so-called “stuck” traffic problem, in which a vehicle in front suddenly changes lanes and slows traffic. They were able to find a solution to this 1,000 times faster than when using their previous Monte Carlo methods.

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10,000 Different Designs

With the use of AI / ML, we are entering a world of generative design, exploring 10,000 different designs based on specifications and quickly simulating them with high-performance computing and Ansys Cloud to provide the best option to the designer. 

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WEBINARS

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High Coverage, Multivariable Build Quality Metrics in Power Integrity Signoff

Semiconductor chips for next-generation automotive, mobile and high-performance computing applications — powered by AI and machine learning algorithms — require the use of advanced 16/7nm systems-on-chips (SoCs), which are bigger, faster and more complex.

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5G Design Innovation Through Simulation

This presentation showcases the past, present and future of mobile networking, and how the convergence of 5G, edge computing and artificial intelligence machine learning will change the industry landscape. 

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SkyAngels and Ansys

See how Ansys simulation assists SkyAngels in developing computational intelligence for autonomous aerial vehicles, aimed at navigating in non-segregated airspace in a predetermined certification path.

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Featured Resources

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RESOURCES

Driving a Radar Revolution arbe

Innovations in Ansys optiSLang

optiSLang’s deep learning extension adds neural networks to metamodel of optimal prognosis (MOP) competition, enabling you to quickly and accurately analyze very large data sets. This proves especially helpful if you are developing advanced driver assistance system

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Ansys Multiphysics Solutions Achieve Certification for TSMC’s N3 and N4 Process Technologies

This enables joint customers to meet critical power, thermal and reliability standards for highly sophisticated artificial intelligence/machine learning, 5G, high-performance computing (HPC), networking and autonomous vehicle chips.

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An Integrated Simulation Platform to Validate Autonomous Vehicle Safety

Today’s hands-off autonomous driving systems are largely built with deep learning algorithms that can be trained to make the right decision for nearly every driving situation. These systems, however, lack the detailed requirements and architecture that have been used up to now to validate safety-critical software, such as that which controls commercial airliners.

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ARTICLES

VIDEOS

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How Industrial IoT is Being Reshaped by 5G, AI, Edge, Cloud, and HPC

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