Non-intrusive double-greedy parametric model reduction by interpolation of frequency-domain rational surrogates
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One of the main goal of Artificial Intelligence is to develop models capable of providing valuable predictions in real-world environments. In particular, Machine Learning (ML) seeks to design such models by learning from examples coming from this same envi ...
Non-convex constrained optimization problems have become a powerful framework for modeling a wide range of machine learning problems, with applications in k-means clustering, large- scale semidefinite programs (SDPs), and various other tasks. As the perfor ...
The last few years have experienced the emergence of Industry 4.0 (I4.0), ultra-customization, and the explosion of demand for ethical, fair trade, and sustainable consumption. Organizations have therefore started a digital transformation of their SCs and ...
Machine intelligence greatly impacts almost all domains of our societies. It is profoundly changing the field of mechanical engineering with new technical possibilities and processes. The education of future engineers also needs to adapt in terms of techni ...
We develop new tools to study landscapes in nonconvex optimization. Given one optimization problem, we pair it with another by smoothly parametrizing the domain. This is either for practical purposes (e.g., to use smooth optimization algorithms with good g ...
Although different vehicle sharing systems (VSSs) use different vehicle types, the management challenges and optimization problems to be solved are similar or even the same. This observation led us to create a generalized and holistic VSS management framew ...
We propose Kernel Predictive Control (KPC), a learning-based predictive control strategy that enjoys deterministic guarantees of safety. Noise-corrupted samples of the unknown system dynamics are used to learn several models through the formalism of non-pa ...
The ongoing global warming situation has bolstered interests in developing and reinforcing green energy. One of the most promising fields is hydropower (Ahmad and Hossain, 2020 and Yazdi and Moridi, 2018). Many existing reservoirs have untapped potential t ...
Bayesian Optimization (BO) is typically used to optimize an unknown function f that is noisy and costly to evaluate, by exploiting an acquisition function that must be maximized at each optimization step. Even if provably asymptotically optimal BO algorith ...
Curie's principle states that "when effects show certain asymmetry, this asymmetry must be found in the causes that gave rise to them." We demonstrate that symmetry equivariant neural networks uphold Curie's principle and can be used to articulate many sym ...