| Students understand and are able to apply Scientific Machine Learning (SciML) techniques, integrating machine learning with scientific computing to solve complex physical, biological, and engineering problems. They can design and implement hybrid models that combine data-driven approaches with domain knowledge, such as differential equations and physical laws, to improve model accuracy, interpretability, and generalization.
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- Applying Scientific Machine Learning to Real-World Problems (k5)
Students are able to implement SciML methods in applications such as climate modeling, materials science, and drug development, where traditional machine learning alone is insufficient.
- Integrating Differential Equations and Machine Learning (k5)
Students can incorporate domain knowledge, such as partial differential equations (PDEs) and physical constraints, into machine learning models to improve predictive accuracy and reliability.
- Implementing Physics-Informed Neural Networks (PINNs) (k3)
Students are able to design and train physics-informed neural networks that integrate scientific principles with deep learning architectures.
- Utilizing Neural Operators for Scientific Computing (k4)
Students can apply neural operators to solve high-dimensional and complex scientific problems, enabling efficient simulations in physics-based AI models.
- Optimizing Hybrid Machine Learning Models (k5)
Students are capable of optimizing and evaluating SciML models, balancing data-driven learning with theoretical knowledge to enhance generalization and robustness.
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Students know of Scientific Machine Learning (SciML), including neural operators, physics-informed neural networks (PINNs), and the integration of partial differential equations into AI models. They know how these techniques enhance traditional machine learning approaches by incorporating scientific principles, improving model interpretability and performance in domains with scarce or noisy data.
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