Driving Engineering Simulation and Design with AI/ML
Tuesday, July 11, 2023
Traditionally, engineered products were designed with mechanical and electrical CAD tools, simulated and validated for correctness with CAE tools, prototypes were fabricated and tested, and products were then manufactured at scale in factories. This process required long product cycles often spanning years to build a new product. Today, virtually unlimited computing and storage available from the cloud is available for generative design to explore 10,000 design choices in near real-time, verify these products accurately through simulation (eliminating the need to build physical prototypes) and manufacture the products using additive manufacturing and factory automation. In the past, simulation tools were used to model specific, solitary physics such as mechanical structures, fluid dynamics, or electromagnetic interactions by solving second order partial differential equations using numerical methods. Today, simulation tools solve multi-physics problems (fluid-structure-electromagnetics interactions) at scale using the most complex solvers. We will explore the use of AI, Machine Learning and Deep Learning to accelerate these engineering simulations. We have identified four broad use cases of AI/ML applied to simulation: (1) Automatic parameter selection of simulation solvers to improve workflows and designer productivity (2) Augmenting simulation with AI/ML to accelerate simulation by factors of 100X (3) The use of AI/ML based generative design techniques to explore 10,000 designs automatically (4) Business intelligence to help improve engineering workflows. My talk will address three broad categories of AI/ML applied to simulation. (1) Top-down methods where we apply an AI/ML framework to a black box solver to train the ML models to improve run time (2) Bottom-up methods where we deeply embed the AI/ML methods inside the physics of our solvers. (3) Reduced order models where the order(?) of the solutions is reduced using AI/ML methods. We will illustrate each of these approaches on existing, commerical tools. As an example of a bottom-up approach, we will describe an ML-based Partial Differential Equation solver and apply it to accelerate Fluid Dynamics problems and will report our results on our Fluent CFD software. As an example of a top-down method, we will report on an ML framework to improve the productivity of any ML developer working in simulation. As an example of a reduced order model we will report on a hybrid digital twin tool called the Twin Builder. We will report on an end-to-end chip packaging solution using a combination of data-driven and physics-informed neural networks, as integrated within Ansys Redhawk/IcePak/Mechanical solutions for Conjugate Heat Transfer. We will describe approaches to support fast design exploration/optimization using ML frameworks. We will describe ML-enabled assistance in various steps of simulation workflows such as initial meshing, smart sub-modeling, user experience and automatic selection of parameters. We will report on automatically setting the best parameters in Fluent/Live AMG solver.
ABOUT: Prith Banerjee is the Chief Technology Officer of Ansys where he is responsible for leading the evolution of Ansys’ Technology strategy and champion the company’s next phase of innovation and growth Formerly, he was Executive Vice President, Chief Technology Officer of Schneider Electric. Previously, Prith was Managing Director of Global Technology Research and Development at Accenture. Formerly, he was Chief Technology Officer and Executive Vice President of ABB. Earlier, he was Senior Vice President of Research at HP and Director of HP Labs. Formerly, Prith was Dean of the College of Engineering at the University of Illinois at Chicago. Formerly, he was the Walter P. Murphy Professor and Chairman of Electrical and Computer Engineering at Northwestern University. Prior to that, Prith was Professor of Electrical and Computer Engineering at the University of Illinois at Urbana-Champaign. In 2000, he founded AccelChip, a developer of products for electronic design automation, which was acquired by Xilinx Inc. in 2006. During 2005-2011, he was Founder, Chairman and Chief Scientist of BINACHIP Inc., a developer of products in electronic design automation. FastCompany listed Prith in their 100 top business leaders in 2009. He is a Fellow of the AAAS, ACM and IEEE, and a recipient of the 1996 ASEE Terman Award, the 2001 Taylor Booth Award from IEEE, and the 1987 NSF Presidential Young Investigator Award. Prith earned a B.Tech. in electronics engineering from the Indian Institute of Technology, Kharagpur, and an M.S. and Ph.D. in electrical engineering from the University of Illinois, Urbana.