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SUMMARY:Thriving in an AI & Emerging Tech World: Opportunities and Challenges for Women Engineers
DESCRIPTION:IEEE Women in Engineering Oregon Section Affinity Group is organizing a panel titled "Thriving in an AI & Emerging Tech World: Opportunities and Challenges for Women Engineers" to celebrate IEEE WIE Day. This engaging and timely discussion will examine how automation\, artificial intelligence\, digital transformation\, and other emerging technologies are reshaping engineering roles and redefining traditional career paths. It will bring together esteemed engineers\, researchers\, and industry leaders to share insights on the impact of AI and automation on engineering careers\, the unique challenges and opportunities these changes present for women engineers\, and strategies for continuous skill development\, career growth\, and leadership in a rapidly transforming technological landscape.\nSpeaker(s): Prof. Saifur Rahman\, Dr. Winnie Ye \, Celia Shahnaz\, John D. McDonald\, P.E\, Jill Gostin\, David Koehler\nVirtual: https://events.vtools.ieee.org/m/486034
URL:https://svec.org/event/thriving-in-an-ai-emerging-tech-world-opportunities-and-challenges-for-women-engineers/
LOCATION:Virtual: https://events.vtools.ieee.org/m/486034
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SUMMARY:Machine Learning-assisted Physics-based Simulation & Control of Flexible Structures\, Sensors and Soft Robots
DESCRIPTION:Advances in flexible\, deployable\, and deformable structures and sensors require efficient simulation tools that capture nonlinear geometry and material behavior. We propose a machine learning (ML) approach using neural networks (NN) to simplify simulations\, enabling the creation of digital twins and facilitating sim-to-real transfer in structural mechanics.\nThis talk presents a case study using neural networks (NN) to create a reduced-order model for the dynamic simulation of a slinky\, a popular children’s toy made of a pre-compressed helical spring that can stretch and deform. Instead of simulating the entire 3D structure of the slinky\, we use a reduced representation based on the deformation of its helix axis\, significantly reducing the degrees of freedom (DOFs). The mechanics of this simplified representation are captured using a neural ordinary differential equation (neural ODE)\, trained with data from high-resolution 3D simulations. This approach enables faster dynamic simulations while maintaining physical accuracy\, and thanks to the physics-based nature of our model trained with neural ODEs\, it is highly generalizable—adapting to changes in boundary conditions or external forces without the need for retraining.\nThe second part of the talk introduces DiSMech\, an open-source software platform for fast simulations of flexible structures\, which was used in the slinky study. DiSMech aims to enable researchers at all levels to explore the mechanics of soft robots and flexible structures/sensors\, driving innovation in robotics research and education. Built on a discrete differential geometry (DDG) approach\, it offers a practical alternative to computationally intensive conventional simulation tools.\nSpeaker(s): Dr. M. Khalid Jawed\nAgenda:\n6:50 – 7 PM: Registration\n7-8 PM: Talk and Q&A\nVirtual: https://events.vtools.ieee.org/m/489214
URL:https://svec.org/event/machine-learning-assisted-physics-based-simulation-control-of-flexible-structures-sensors-and-soft-robots/
LOCATION:Virtual: https://events.vtools.ieee.org/m/489214
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