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Modeling with Machine Learning

Aug. - Dec. 2024

 

Two projects done in a class covering machine learning tools for modeling energy transport and conversion processes. Final project was self-directed to explore time-series data prediction.

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  • Machine Learning

  • Modeling

  • Thermodynamics

What I did:

  • Project 1: Photovoltaic (PV) Cells​

    • Training neural network model to predict the voltage-current relationship for a PV cell based on operating characteristics

    • Training a second model to predict which configuration in a 4-PV-cell system maximizes power output

    • Validating both models to achieve a mean absolute error below 0.03

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Training and test loss as function of epoch when implementing a learning rate scheduler in Keras

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Model-predicted power outputs vs actual PV cell outputs for training dataset, validation dataset, and a dataset outside the range of provided data (Higflux)

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Surface plot of the final neural network model predictions for power output as a function of load resistance, R, and incident direct normal solar radiation intensity, ID

  • Project 2: Physics Inspired Neural Network (PINN) for 2D Heat Conduction

    • Predicting a temperature field for a 2D material domain using machine learning and a custom loss function​

    • Incorporating the custom loss function taking into account thermodynamic boundary conditions, min/max temperatures, and Laplacian interior temperature rules

    • Training the model to predict the temperature field with a loss of 0.009

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Design of custom loss function incorporating boundary conditions, min/max temperature values, and the heat transport equation. (Van P. Carey)

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Illustration of the material being modeled with boundary conditions and positions of temperature readings in black. (Van P. Carey)

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Model-predicted temperature distribution in the material domain

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  • Project 3: Running Speed Estimation from Leg-Work IMU

    • Designing a neural network to predict a runner's speed based on acceleration data from an inertial mass unit (IMU)

    • Explored design parameters for the model including RNN, LSTM, masking, and dropout layers

    • Determined the optimal model configuration to obtain a validation mean absolute error of 4.69​​​​​​​​​​​​​​​

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Running acceleration IMU data my teammate collected to be used in training the model. (Douglas Hutchings)

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Neural network design parameters for the best performing model

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Predictions of the best performing model vs actual treadmill's (and runner's) speed

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