Amrita Basak: Assistant Professor of Mechanical Engineering at Penn State
Topic: Minimizing the Requirement of Expensive Simulation Models Using Inexpensive Ones Via Machine Learning
Predictive modeling can play a powerful role in accelerating the fundamental understanding of additive manufacturing processes. Depending on how much physics these models are trying to resolve, they can be expensive and accurate (e.g., a finite element model) or inexpensive and inaccurate (e.g., an analytical model). In this talk, I will discuss how different models belonging to different fidelity (and precision) levels could be blended so that the inexpensive models are exploited more than the expensive models, but the overall accuracy is not compromised.
Dr. Amrita Basak is an Assistant Professor in the Department of Mechanical Engineering at the Pennsylvania State University – University Park. Dr. Basak's research group performs research at the intersection of additive manufacturing, materials characterization, computational modeling, and machine learning involving metallic, piezoelectric ceramic, and biopolymer materials. Dr. Basak is a co-ambassador of the “Women in 3D Printing (Wi3DP)” chapter at Penn State.
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