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Found 7 papers, 1 papers with code
Synchronized states of power grids and oscillator networks by convex optimization
Synchronization is essential for the operation of AC power systems: All generators in the power grid must rotate with fixed relative phases to enable a steady flow of electric power.
Transgressing the boundaries: towards a rigorous understanding of deep learning and its (non-)robustness
Nevertheless, it is evident that certain specifics of DL that could explain its success in applications demands systematic mathematical approaches.
Long Short-Term Memory Neural Network for Temperature Prediction in Laser Powder Bed Additive Manufacturing
This is realized by employing heat maps produced by an optimized printing protocol simulation and used for training a specifically tailored recurrent neural network in terms of a long short-term memory architecture.
$α$-Hypergeometric Uncertain Volatility Models and their Connection to 2BSDEs
In this article we propose a $\alpha$-hypergeometric model with uncertain volatility (UV) where we derive a worst-case scenario for option pricing.
Model reduction and uncertainty quantification of multiscale diffusions with parameter uncertainties using nonlinear expectations
For systems with unknown diffusion coefficient, the underlying stochastic control problem admits an interpretation in terms of a stochastic differential equation driven by a G-Brownian motion.
Optimization and Control Probability 60G65 (Primary), 70K70, 93E20 (Secondary)
Variational approach to rare event simulation using least-squares regression
We propose an adaptive importance sampling scheme for the simulation of rare events when the underlying dynamics is given by a diffusion.
Probability Optimization and Control 65C05 (primary), 65C30, 92C40 (secondary)
Learning Inverse Statics Models Efficiently
Moreover, we exploit symmetries in the inverse statics mappings to drastically reduce the number of samples required for learning inverse statics models.
