In the influence maximization problem we are interested in finding a given set of k vertices to activate in order to maximize the expected number of nodes that will eventually get activated in the network through a diffusion process. For this diffusion we assume the linear threshold model. We propose a new Clustering Greedy Algorithm (C-Greedy). The C-Greedy algorithm applies the Monte Carlo...
Energy storage systems have become a promising option to increase power system flexibility and harness larger shares of variable renewable energy. To get a full picture of their potential operation and benefits, a realistic representation of their characteristics is essential in power system models. Energy storage models require binary variables to correctly model reserves and to ensure that...
Bayesian optimization is a family of methods for sample efficient optimization of noisy, black box, expensive to evaluate functions. The approach of these methods is to maintain a random field, usually a Gaussian process, to model uncertainties in values of the objective function. This model is refined throughout optimization and is used for deciding where to evaluate the objective function...
Metabolic models are typically characterized by a large number of parameters. Traditionally, metabolic control analysis is applied to differential equation based models to investigate the sensitivity of predictions to parameters. A corresponding theory for constraint based models is lacking, due to their formulation as optimization problems. Optimal solutions of optimization problems can be...
Sparse Identification of Nonlinear Dynamics (SINDy) is a powerful method to discern the underlying model from data, as it utilizes modern machine learning techniques and sparse regression to discover dynamics of the system. Brunton et al. in his pioneering work proposed several optimization algorithms to solve this problem. Motivated by the nature of FitzHugh-Nagumo simplified model of the...
Microgrid sizing optimization is often formulated as a black-box optimization problem. This allows modeling the microgrid with a realistic temporal simulation of the energy flows between components. Such models are usually optimized with gradient-free methods, because no analytical expression for gradient is available. However, the development of new Automatic Differentiation (AD) packages...
Recently, Graph Neural Networks have been very successful in solving machine learning tasks such as classification and prediction at the level of nodes, edges and graphs. Their temporal version handles data that evolve over time, such as pandemics and traffic, social networks, financial time series, and brain activity time series. In this poster, I will present how we have implemented some...
