ML/DO 5: Hybrid Machine Learning and Physics

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Week 5: Hybrid Machine Learning and Physics-based Modeling

Machine Learning and Dynamic Optimization is a course on the theory and applications of numerical solutions of time-varying systems with a focus on engineering design and real-time control applications. Concepts taught in this course include physics-based and empirical modeling, machine learning classification and regression, nonlinear programming, estimation, and advanced control methods such as model predictive control. The course is freely available and can be accessed online with all source code and solution videos for self-paced learning.

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I've learnt so much! please keep going <3

serajsersawi

Good day, sir! First of all, I'm so sorry for too much informality of contacting you through commenting on your YouTube video.

I'm a student from the Philippines, and currently conducting a study about the effectiveness of the public health countermeasures in controlling the spread of Covid-19 in one of the municipalities here in the Philippines.

Since our study will be focusing on the effectiveness of public health countermeasures we will be utilising the SEIR model. At the moment, we haven't found someone knowledgeable enough in using this model.

Just this day, I watched your video regarding the SEIR model. With this, I am asking you, sir, to become our analyst to the data we have gathered and be able to stimulate these data, same as what you've done in the video about the SEIR model, and finally interpret if those interventions are effective.

Again, I'm so sorry for too much informality I've caused. I am hoping that you will reply to this comment, sir.

Thank you and more power!

joearnelfabricante

ML/DO 5: Hybrid Machine Learning and Physics

ML/DO 5: Hybrid Machine Learning and Physics

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