Artificially Intelligent Manufacturing (AIM) Paradigm for Composites EFRC Center

We are excited to see AIM EFRC Director Dr. Srikanth Pilla featured as a panelist at #CAMX2026 for “Bridging the Gap: Making Academic Research Work for Industry.” The session will bring together academic and industry leaders to discuss how research innovations can be translated into real-world composite applications and accelerated toward industry adoption. 📅 Wednesday, September 23 🕒 3:30-4:00 PM 📍 CAMX Innovation Stage Learn more and register: https://lnkd.in/erwR43De #CAMX2026 #Composites #AdvancedManufacturing #MaterialsScience #Innovation #Research #Industry

Artificial intelligence is transforming composite manufacturing, and the latest research from AIM EFRC demonstrates a powerful new framework for predicting and reducing manufacturing-induced deformation in carbon fiber composites. Title: Probabilistic Predictions of Process-Induced Deformation in Carbon/Epoxy Composites Using a Deep Operator Network Read the full paper: https://lnkd.in/e8aDrEXN What is this about? Composite materials can deform during curing due to residual stresses from thermal expansion and resin shrinkage. This study combines physics-based modeling, experiments, and AI to predict process-induced deformation (PID) across a range of cure conditions. Deep Operator Networks (DeepONets), transfer learning, and uncertainty quantification are used to improve predictions and optimize cure processes. Key highlights: • Developed a DeepONet surrogate model for predicting process-induced-deformation. • Combined high-fidelity simulations with experimental data using transfer learning. • Used Ensemble Kalman Inversion (EKI) for uncertainty quantification. • Predicted degree of cure, viscosity, and deformation throughout manufacturing. • Optimized cure schedules to reduce residual stresses and deformation. Why it matters? Accurately predicting manufacturing-induced deformation can improve dimensional accuracy and reduce costly trial-and-error during composite manufacturing. This AI-enabled framework supports more reliable and efficient cure process optimization. Relevance to AIM EFRC: This work advances AIM EFRC's mission to build AI-enabled approaches for understanding the material-process-performance (MP²) relationship in advanced polymer composites. By integrating high-fidelity simulations, targeted experiments, and Deep Operator Networks, the study strengthens the Center's Digital Life Cycle framework for predicting manufacturing behavior and enabling more reliable, energy-efficient composite manufacturing. Collaboration Credit: This work is a collaboration between Brown University (Elham kiyani, Zhiwei Gao, Zongren Zou, George Karniadakis), the University of Delaware (Amit M. Deshpande, Sai Aditya Pradeep, Ph.D., Srikanth Pilla), and Clemson University (Madhura Limaye, Ph.D., Gang Li, Zhen Li). About AIM: AIM is an innovative multidisciplinary research center funded by the U.S. Department of Energy Office of Science, under grant no. DE-SC0023389. U.S. Department of Energy (DOE), University of Delaware, UD Center For Composite Materials, Clemson University, Clemson University College of Engineering, Computing and Applied Sciences, Clemson University International Center for Automotive Research, The Ohio State University, Brown University, University of Florida, South Carolina State University, Pacific Northwest National Laboratory, Savannah River National Laboratory. #CompositeManufacturing #ArtificialIntelligence #DeepLearning #MaterialsScience #MaterialsModeling

Congratulations to Dr. Yong Huang! AIM EFRC proudly congratulates Dr. Yong Huang on his promotion to Distinguished Professor at the University of Florida, effective August 16, 2026. This prestigious honor recognizes his exceptional contributions to research, education, and service. The title of Distinguished Professor is a rare and prestigious achievement at the University of Florida, recognizing faculty members with an exceptional record of achievement in teaching, research and publication, and professional and public service. This honor recognizes individuals whose accomplishments demonstrate significant distinction and national and international recognition within their field. As a principal investigator representing the University of Florida in AIM EFRC, Dr. Huang contributes to Research Thrust III, advancing innovative manufacturing approaches that support the Center's mission to accelerate materials discovery. We are proud to celebrate this well-deserved achievement and grateful for Dr. Huang's leadership and contributions to AIM. Congratulations! About AIM: AIM is an innovative multidisciplinary research center funded by the U.S. Department of Energy Office of Science, under grant no. DE-SC0023389. U.S. Department of Energy (DOE), University of Delaware, UD Center For Composite Materials, Clemson University, Clemson University College of Engineering, Computing and Applied Sciences, Clemson University International Center for Automotive Research, The Ohio State University, Brown University, University of Florida, South Carolina State University, Pacific Northwest National Laboratory, Savannah River National Laboratory. #AIMEFRC #UniversityofFlorida #DistinguishedProfessor #AdvancedManufacturing #MaterialsScience #ResearchExcellence

📢 Survey Extension: Help Shape the Future of Composites Education & Workforce Development Thank you to everyone who has already shared their feedback on CCM’s educational and training program survey. We have received valuable input from the composites community and appreciate your insights in helping us understand industry needs and priorities. To allow more time for participation, we are extending the survey deadline to August 15, 2026. We are continuing to gather feedback on: • Preferred formats for professional education and training • Technical topics and skills most valuable to the composites workforce • Emerging technologies shaping the future of the industry • Opportunities for CCM to better support industry and academic partners The survey takes approximately 2-3 minutes to complete. 📝 Share your input here: https://brnw.ch/21x4yC2 Thank you for helping us develop future CCM educational and training opportunities!

If you're part of the composites community, please take 2-3 minutes to complete this survey from the UD Center For Composite Materials (CCM). Your feedback will help shape future workshops, certificate programs, and workforce development initiatives. Survey Link: https://brnw.ch/21x4kOF Please consider sharing this with your network as well to help us reach a broader audience. Thank you! About AIM: AIM is an innovative multidisciplinary research center funded by the U.S. Department of Energy Office of Science, under grant no. DE-SC0023389. U.S. Department of Energy (DOE), University of Delaware, UD Center For Composite Materials, Clemson University, Clemson University College of Engineering, Computing and Applied Sciences, Clemson University International Center for Automotive Research, The Ohio State University, Brown University, University of Florida, South Carolina State University, Pacific Northwest National Laboratory, Savannah River National Laboratory.

Help Shape the Future of Composites Education & Workforce Development The UD Center For Composite Materials (CCM) is exploring new educational and professional training opportunities for the composites community, and we would like your input. We are evaluating potential offerings such as: • Hands-on technical workshops • Professional certificate programs • Microcredentials and modular (Short, flexible learning modules) We are interested in understanding: • The technical topics that matter most to you • Your preferred learning format (online, in-person, conference-based, or hybrid) • Emerging technologies that will shape the composites industry over the next 5-10 years • How CCM can best support workforce development and industry needs The survey takes approximately 2-3 minutes to complete. Your feedback will help guide the development of future CCM educational and training programs. Please complete the survey by July 30, 2026 to help us plan our 2027 educational workshops and training programs. Take the survey here: https://brnw.ch/21x4kOF #UDel #CCM #EducationandTraining #Survey #Composites #CoursesandCertificatePrograms #Workshops

Catch out last webinar for the Summer Research Internship Program, "Hands-on tutorial 2: Applying Physics-Informed Neural Network (PINN) to Solve Partial Differential Equations in Heat Transfer", today at 4pm.

Our Summer Internship Program Hands-on Tutorial Series concludes today with an interactive session on Physics-Informed Neural Networks (PINNs) and their application to solving heat transfer problems. If you're interested in the intersection of artificial intelligence, computational science, and engineering, don't miss this opportunity to learn from Haizhou Wen through a practical demonstration using DeepXDE. 🗓️ Today 🕓 4:00-5:00 PM EST 💻 Virtual (Zoom) There's still time to register and join us: 🔗 https://lnkd.in/ewMHVr7T Thank you to everyone who has participated throughout this year's Summer Internship Program tutorial series. We look forward to closing out the series with another engaging session! Acknowledgements: This webinar is co-produced by Artificially Intelligent Manufacturing (AIM) Paradigm for Composites EFRC Center funded by the U.S. Department of Energy Office of Science, Basic Energy Sciences under awards (AIM EFRC DE-SC0023389) and Inverse Design Training in Advanced and Sustainable Composites - NSF Research Traineeship under award #2244342 from NSF’s Division of Graduate Education. U.S. Department of Energy (DOE), University of Delaware, UD Center For Composite Materials, Clemson University, Clemson University College of Engineering, Computing and Applied Sciences, Clemson University International Center for Automotive Research, The Ohio State University, Brown University, University of Florida, South Carolina State University, Pacific Northwest National Laboratory, Savannah River National Laboratory. #AIMEFRC #MachineLearning #PINNs #ScientificMachineLearning #EngineeringResearch #SummerInternship #STEM #DOE #NSF #Webinar

Curious how artificial intelligence is transforming engineering research? Join us this Monday for the final session of our Summer Internship Program Hands-on Tutorial Series, where we'll explore how Physics-Informed Neural Networks (PINNs) are being used to solve complex heat transfer problems. 💡 In this session, participants will: ✔️ Learn the fundamentals of PINNs ✔️ Explore how machine learning can solve partial differential equations ✔️ Gain hands-on experience with DeepXDE, an open-source library for scientific machine learning 📅 Monday, July 13, 2026 🕓 4:00-5:00 PM EST 💻 Virtual (Zoom) 🔗 Register here: https://lnkd.in/ewMHVr7T Whether you're a student, researcher, or engineer interested in AI, computational modeling, or scientific computing, this webinar offers practical insights into one of today's fastest-growing research areas. We look forward to seeing you there! Acknowledgements: This webinar is co-produced by Artificially Intelligent Manufacturing (AIM) Paradigm for Composites EFRC Center funded by the U.S. Department of Energy Office of Science, Basic Energy Sciences under awards (AIM EFRC DE-SC0023389) and Inverse Design Training in Advanced and Sustainable Composites - NSF Research Traineeship under award #2244342 from NSF’s Division of Graduate Education. U.S. Department of Energy (DOE), University of Delaware, UD Center For Composite Materials, Clemson University, Clemson University College of Engineering, Computing and Applied Sciences, Clemson University International Center for Automotive Research, The Ohio State University, Brown University, University of Florida, South Carolina State University, Pacific Northwest National Laboratory, Savannah River National Laboratory. #AIMEFRC #MachineLearning #PhysicsInformedAI #PINNs #DeepLearning #HeatTransfer #ComputationalScience #STEMEducation #Webinar #Research

🌟 Join us for the final session of our Summer Internship Program Hands-on Tutorial Series! 🌟 🧠 Title: Hands-on tutorial 2: Applying Physics-Informed Neural Network (PINN) to Solve Partial Differential Equations in Heat Transfer 🗓️ Date: July 13, 2026 🕓 Time: 4:00 – 5:00 PM EST 📍 Location: Virtual (Zoom) 🔗 Register Here to Secure Your Spot: https://lnkd.in/ewMHVr7T This introductory webinar series explores the transformative role of neural networks (NNs) in science and engineering, with a focus on their growing potential to revolutionize data analysis and computational modeling. As a powerful branch of artificial intelligence, neural networks serve as surrogate models capable of efficiently approximating complex systems with high accuracy. This Webinar will delve into more advanced topics, specifically the use of Physics-Informed Neural Networks (PINNs) for solving partial differential equations in heat transfer problems. A practical tutorial on using DeepXDE—an open-source deep learning library for solving differential equations—will be included to equip participants with applicable tools and skills. 🎓 About the Speaker: Haizhou Wen conducts research on meso-scale modeling, complex fluids, and machine learning applications in engineering and biology, with work published in Biophysical Journal and Physics of Fluids. He is advised by Prof. Zhen Li in the MuthComp Group. Acknowledgements: This webinar is co-produced by Artificially Intelligent Manufacturing (AIM) Paradigm for Composites EFRC Center funded by the U.S. Department of Energy Office of Science, Basic Energy Sciences under awards (AIM EFRC DE-SC0023389) and Inverse Design Training in Advanced and Sustainable Composites - NSF Research Traineeship under award #2244342 from NSF’s Division of Graduate Education. In collaboration with: University of Delaware, UD Center For Composite Materials, Clemson University, Clemson University College of Engineering, Computing and Applied Sciences, Clemson University International Center for Automotive Research, The Ohio State University, Brown University, University of Florida, South Carolina State University, Pacific Northwest National Laboratory, Savannah River National Laboratory. #AIMEFRC #MachineLearning #NeuralNetworks #ComputationalModeling #STEMEducation #SummerInternship #Webinar #StudentResearch #DOE #NSF