4 papers
Adversarial Agents on Topology Optimization: Understanding the Fragility and Robustness of Deep Learning-based and Physics-Based Design Models under Adversarial Perturbation
Hoang Anh Nguyen, Yuan Hong, Hongyi Xu
Topology optimization, using both physic-based approaches and deep learning surrogates, serves as a cornerstone for generative design agents in cyber-manufacturing systems. While d…
Physics-Distilled Neural Network enabled by Large Language Models for Manufacturing Process-Property Predictive Modeling
Ge Song, Kiarash Naghavi Khanghah, Anandkumar Patel +2
Predicting process-property relationships in manufacturing is often challenged by high experimental costs and the limited interpretability of complex 'black-box' models. This paper…
GPT-Micro: A large language paradigm for accelerated, inexpensive, and thermodynamics-consistent discovery of constitutive models in manufacturing
Soumik Dutta, Kiarash Naghavi Khanghah, Sania Shree +4
Constitutive modeling of the relationship between process-imposed material states and fundamental material properties is critical to control of material microstructure in manufactu…
Large Language Models for Extrapolative Modeling of Manufacturing Processes
Kiarash Naghavi Khanghah, Anandkumar Patel, Rajiv Malhotra +1
Conventional predictive modeling of parametric relationships in manufacturing processes is limited by the subjectivity of human expertise and intuition on the one hand and by the c…