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Scientific Machine Learning What are the opportunities and challenges of machine learning in complex applications across science, engineering, and medicine?
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Scientific Machine Learning - Where Physics-based Modeling Meets Data-driven Learning
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- 1 Scientific Machine Learning Where Physics-based Modeling Meets Data-driven Learning
- 2 Scientific Machine Learning What are the opportunities and challenges of machine learning in complex applications across science, engineering, and medicine?
- 3 How do we harness the explosion of data to extract knowledge, insight and decisions?
- 4 Example: modeling combustion in a rocket engine Conservation of mass (p), momentum (w), energy (E)
- 5 There are multiple ways to write the Euler equations
- 6 Introducing auxiliary variables can expose structure - lifting
- 7 Lifting example: Tubular reactor
- 8 Modeling a single injector of a rocket engine combustor
- 9 Performance of learned quadratic ROM
- 10 Data-driven decisions
