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| ME 53900 - Introduction To Scientific Machine Learning |
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Associated Term:
Spring 2024
Learning Outcomes: 1. Represent mathematically the uncertainty in the parameters of physical models. 2. Propagate parametric uncertainty through physical models to quantify the induced uncertainty in quantities of interest. 3. Calibrate the uncertain parameters of physical models using experimental data. 4. Combine multiple sources of information to enhance the predictive capabilities of models. 5. Pose and solve design optimization problems under uncertainty involving expensive simulations or experiments. 6. Improve scientific writing and data visualization skills. Required Materials: Technical Requirements: |
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