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Upcoming Seminars

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Machine-Learning for Modeling Complex Materials and Media

Date: June 18, 2021 1:00 pm Pacific
Speaker: Serveh Kamrava (USC)

In recent years, machine learning (ML) approaches have made it possible to extract and explore intricate patterns from big data. One of the fields that can benefit from the computational advantages that ML offers is materials characterization where we have complex heterogeneous morphology. The morphology of complex systems is one of the determinant elements that control a variety of their properties, such as flow, transport, and mechanical behaviors. Such properties are often estimated using experimental and computational methods, which can be very costly and time-demanding. As such, faster and more automatic methods are required. Machine learning provides an alternative solution for this problem. In this presentation, I will present a deep learning method that can take the 3D morphology of complex materials and estimate their transport properties. Then, I will talk about a novel method using which one can quantify the accuracy of augmentation methods for adding more data to ML and identify the method that can provide the best set of data by minimizing the discrepancy and expanding the variability. For the next topic, I will discuss the application of deep learning for dynamic data when they change with time for a transport problem on a complex membrane system. I close this particular topic by describing how the governing equations can be used in ML for filling the gap in data and reducing the amount of data for ML. These results will be compared with a fully data-driven ML method.

Deep Learning for Anomaly Detection

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