Physicsaware machine learning (being updated)

We develop and apply a variety of computational techniques, based on artificial intelligence, machine learning, and weather forecast methods, for accurate physical description, prediction and control of rare and extreme events. In lack of a full physical description, existing database and experimental data will be used to develop hybrid predictive tools, which will be physicsbased and datadriven. The broad objectives are:
1. Modelling combustion systems with artificial intelligence. Combustion problems are governed by physical laws. Thus, we will continue to make use of mathematical models based on conservation laws! However, to minimize the uncertainties of our models, we propose to use data from existing experimental and computational databases that are either publicly available or are produced within our group. In order for the model to become more accurate given the information from the external data, we will use artificial intelligence and machine learning algorithms. In particular, spectral proper orthogonal decomposition (in collaboration with Prof. Oliver Schmidt, UCSD), clustering (in collaboration with Prof. Peter Schmid, Imperial College London), Bayesian classification and manifold learning. The deliverable will be a software that is able to (i) selfreprogram (with supervision) anytime that external training data/information/knowledge is inputted, and (ii) recognize if there are unacceptably large uncertainties, hence, select another appropriate model in real time.
2. Prediction and prevention of extreme events. Once a robust model is selected, the parameters will be estimated through data assimilation and online machine learning. Data assimilation is a technique that is used in weather forecasting to update the forecast from numerical simulations with data from observatories, which is sparse in space and time. We propose to use data assimilation based on stochastic updating and Lagrangian optimization to (i) quantify the leastbiased reactingflow parameters, and (ii) evaluate the degree of confidence (uncertainty) of the parameters and predictions.
1. Modelling combustion systems with artificial intelligence. Combustion problems are governed by physical laws. Thus, we will continue to make use of mathematical models based on conservation laws! However, to minimize the uncertainties of our models, we propose to use data from existing experimental and computational databases that are either publicly available or are produced within our group. In order for the model to become more accurate given the information from the external data, we will use artificial intelligence and machine learning algorithms. In particular, spectral proper orthogonal decomposition (in collaboration with Prof. Oliver Schmidt, UCSD), clustering (in collaboration with Prof. Peter Schmid, Imperial College London), Bayesian classification and manifold learning. The deliverable will be a software that is able to (i) selfreprogram (with supervision) anytime that external training data/information/knowledge is inputted, and (ii) recognize if there are unacceptably large uncertainties, hence, select another appropriate model in real time.
2. Prediction and prevention of extreme events. Once a robust model is selected, the parameters will be estimated through data assimilation and online machine learning. Data assimilation is a technique that is used in weather forecasting to update the forecast from numerical simulations with data from observatories, which is sparse in space and time. We propose to use data assimilation based on stochastic updating and Lagrangian optimization to (i) quantify the leastbiased reactingflow parameters, and (ii) evaluate the degree of confidence (uncertainty) of the parameters and predictions.