Physics-informed data-driven frameworks for materials discovery
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This dissertation presents a comprehensive exploration of scientific machine learning methodologies applied to various aspects of material science and additive manufacturing. Chapter 2 introduces a scientific machine learning framework tailored to understand the synthesis process of flash graphene. Leveraging advanced algorithms and data-driven approaches, this framework facilitates a deeper comprehension of the intricate mechanisms involved in flash graphene synthesis, thereby offering valuable insights for optimization and enhancement. In Chapter 3, physics-informed machine learning models are developed for the classification of printability and glass transition temperature (Tg) in additive manufacturing processes. By integrating fundamental principles of physics into the machine learning models, this chapter demonstrates improved accuracy and reliability in predicting printability and Tg. Chapter 4 focuses on physics-constrained multi-objective Bayesian optimization techniques to expedite the 3D printing of thermoplastics. Through the utilization of Bayesian optimization algorithms that incorporate physical constraints, this chapter presents a systematic approach to accelerate the optimization process while maintaining the integrity and stability of printed structures. Collectively, these chapters contribute to the advancement of scientific machine learning methodologies in material science and additive manufacturing, offering novel insights, techniques, and tools for enhancing process understanding, optimization, and control.
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Ph. D.