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Ontologies, Graph Deep Learning, & AI
Abstract
The integration of ontologies, semantic reasoning, and graph-based deep learning and AI signifies a paradigm shift in studying high-dimensional multimodal problems, particularly within advanced manufacturing, synchrotron science, and photovoltaics. Ontologies provide structured frameworks for knowledge representation, while graphs model complex relationships and interactions, enhancing AI’s reasoning and predictive capabilities. In this talk, we explore ‘mds-onto’: a low-level ontology developed for multiple materials science domains such as laser powder bed fusion (LPBF), direct ink writing (DIW), and synchrotron x-ray experiments. Foundation models, which are domain-specific deep learning neural network models trained using self-supervised learning, can be fine-tuned for multiple specific learning tasks. Utilizing spatiotemporal graph neural networks as graph foundation models enables multimodal analysis, wherein preprocessing extracts features from diverse datasets and constructs spatiotemporal graphs with these feature vectors for foundation model training. These ddDTs are capable of answering task-specific questions such as classifying parts with or without pores and ensuring track continuation in LPBF, performing data imputation and regression for error estimation in DIW, and predicting PV powerplant performance, enabling real-time monitoring, predictive maintenance, and optimization of manufacturing processes. Incorporating ontologies and knowledge graphs into ddDTs enhances their intelligence and decision-making capabilities, thereby improving process efficiency and product innovation. This underscores the importance of data-centric AI for ensuring accurate and robust AI models.
Bio
Dr. Pawan Tripathi is a research assistant professor in the Department of Materials Science and Engineering at CWRU in Ohio. He leads projects related to materials data science at the DOE/NNSA-funded Center of Excellence for Materials Data Science for Stockpile Stewardship. His expertise lies in interface structural simulations and developing automated analysis pipelines for large multimodal datasets from diverse experiments. Dr. Tripathi’s current research focuses extensively on data FAIRification, deep learning, image processing, semantic segmentation, and statistical modeling, particularly in the context of advanced manufacturing and laser powder bed fusion.